Analisis Sentimen Twitter Debat Calon Presiden Indonesia Menggunakan Metode Fined-Grained Sentiment Analysis

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Abstrak:

Media sosial, Twitter, saat ini telah banyak memberikan dampak besar dalam membangun opini, pandangan, sentimen, dan preferensi politik publik (menjelang Pemilihan Umum) berlangsung.

Penelitian ini dilakukan untuk mengetahui percakapan di Twitter pada debat pertama calon presiden Republik Indonesia melalui hashtag dari kedua pasang calon.

Selain itu, juga untuk mengetahui tentang kecenderungan masyarakat di Twitter terkait dengan debat yang sedang berlangsung tersebut cenderung positif, negatif, atau netral.

Data percakapan di Twitter didapatkan melalui Twitter API yang diambil dengan bahasa Pemrograman R.

Proses analisis sentimen ini menggunakan metode Fined-grained Sentiment Analysis yaitu, Jika satu tweet berisi lebih banyak kalimat positif daripada negatif, maka hasil keseluruhan akan positif dan bernilai (+1).

Jika jumlah kalimat negatif lebih besar dari kalimat positif, maka hasil keseluruhan negatif dan bernilai (-1).

Jika ada jumlah yang sama dari kalimat positif dan negatif dalam paragraf, maka hasilnya adalah netral dan bernilai (0).

Hasil dari penelitian ini menunjukkan bahwa tweet sentimen dari kedua hashtag cenderung positif, lebih banyak daripada sentimen negatif dan netral.
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Analisis Sentimen Topik Viral Desa Penari Pada Media Sosial Twitter Dengan Metode Lexicon Based

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Abstract :

The horror story of Dancer Village in Indonesia is a viral topic that has become a talk of citizens on Twitter social media.

Various responses and public opinions emerged related to the truth of the story of supernatural experiences of students during a Real Work Lecture in an East Java region of Indonesia.

This study conducted a sentiment analysis of community comments on Twitter social media on the viral topic using the Lexicon Based method.

Sentiment classification is divided into 3 classes namely positive, negative and neutral.

The research phase consists of data collection, pre-processing, processing (sentiment analysis) and visualization.

Data collection uses Twitter  Search  API  with  1000  Penari  Desa  keywords in  Indonesian.

The  lexicon  assessment results from 1000 tweets data obtained 33 positive, 767 neutral and 200 negative.

The percentage of tweets containing positive comments by 3.3%, neutral 76.7% and negative by 20%.

Keywords: Dancer Village, Sentiment Analysis, Lexicon Based, Twitter, WorldCloud

Abstrak  : 

Kisah  horor  Desa  Penari  di  Indonesia  merupakan  topik  viral  yang  menjadi perbincangan  warganet pada  media sosial  twitter.

Berbagai  tanggapan dan  opini masyarakat muncul terkait kebenaran cerita pengalaman supranatural mahasiswa saat Kuliah Kerja Nyata di sebuah wilayah Jawa Timur Indonesia.

Penelitian ini melakukan analisis sentimen dari komentar-komentar  masyarakat  pada  media  sosial  Twitter  terhadap  topik  viral  tersebut  menggunakan metode  Lexicon  Based.

Klasifikasi sentimen  dibagi  menjadi  3  kelas yaitu  positif,  negatif  dan netral.  Tahap  penelitian  terdiri dari  pengumpulan  data, prapengolahan,  pengolahan  (analisis sentimen)  dan  visualisasi.

Pengumpulan  data  menggunakan  API  Search  Twitter  dengan  kata kunci  Desa  Penari  sebanyak  1000  buah  komentar  (tweet)  dalam  bahasa  Indonesia.

Hasil penilaian  leksikon dari  1000 data  tweet diperoleh  33 tweet  bernilai positif,  767 tweet  bernilai netral dan  200 tweet negatif. Prosentase tweet berisi komentar positif sebesar 3.3 %, netral 76.7 % dan negatif sebesar 20%. 

Kata Kunci : Desa Penari, Analisis Sentimen, Lexicon Based, Twitter, WorldCloud
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https://www.researchgate.net/publication/339232872_ANALISIS_SENTIMEN_TOPIK_VIRAL_DESA_PENARI_PADA_MEDIA_SOSIAL_TWITTER_DENGAN_METODE_LEXICON_BASED

Text Mining pada Sosial Media untuk Mendeteksi Emosi Pengguna Menggunakan Metode Support Vector Machine dan K-Nearest Neighbour

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Twitter layanan jejaring sosial dan mikroblog yang memungkinkan penggunanya untuk mengirim dan membaca pesan berbasis teks hingga 140 karakter, yang dikenal dengan sebutan kicauan (tweet).

Sebuah teks pada tweet tidak hanya menyampaikan keterangan dari suatu informasi, tetapi juga berisi informasi tentang perilaku manusia termasuk emosi.

Untuk mendeteksi emosi dari teks pada layanan sosial media twitter dengan data yang tidak terstruktur maka perlu dilakukan analisis teks salah satunya dengan menggunakan Text Mining.

Pada penelitian ini mengusulkan melakukan penelitian text mining pada Sosial Media untuk mendeteksi emosi pengguna.

Deteksi emosi berbasis teks dapat digunakan dalam bisnis, pendidikan, psikologi, dan bidang lain mana pun yang paling penting untuk memahami dan menafsirkan emosi.

Tahapan penelitian ini melalui beberapa tahapan yaitu data. Dari Pengujian yang dilakukan dengan metode Support Vector Machine dan K-Nearest Neighbour dapat menghasilkan nilai rata-rata precision sebesar 0.45640904478933. nilai recall sebesar 0.50199332258158 dan pada nilai accuracy sebesar 0.8140589569161 sedangkan dari metode K-Nearest Neighbour nilai rata-rata precision sebesar 0.34210487225193. nilai recall sebesar 0.45954538381009 dan pada nilai accuracy sebesar 0.79705215419501. hasil dari pengujian dengan metode SVM-KNN menunjukkan bahwa kesesuaian klasifikasi emosi lebih baik daripada metode K-Nearest Neighbour dari keseluruhan kategori emosi.



I. PENDAHULUAN

Data tidak terstruktur banyak terdapat pada layanan sosial media. 

Layanan  sosial  media  merupakan  penyedia  sumber daya yang menyediakan data yang cukup besar.

Media sosial banyak menyita perhatian masyarakat karena dianggap dapat menjadi tempat untk berbagi karya, ide, opini tentang isu-isu yang terjadi  secara bebas, dan media untuk  mengungkapkan berbagai  hal  mengenai  kehidupan  pribadinya. 

Salah  satu media  sosial  yang  banyak  digunakan  masyarakat  adalah Twitter. 

Twitter  layanan  jejaring  sosial  dan  mikroblog  yang memungkinkan penggunanya untuk mengirim dan membaca pesan berbasis teks hingga 140 karakter, yang dikenal dengan sebutan kicauan (tweet)[1].

Sebuah  teks  pada  tweet  tidak  hanya  menyampaikan keterangan dari  suatu informasi,  tetapi  juga berisi informasi tentang perilaku manusia termasuk emosi.

Emosi merupakan keadaan  kompleks  dari  pikiran  yang  dipengaruhi  oleh peristiwa  eksternal,  perubahan  fisiologis,  atau  hubungan dengan orang lain. 

Dengan  tidak adanya kontak  tatap muka untuk  mendeteksi  ekspresi  wajah  dan intonasi  dalam  suara, opsi  alternatifnya  adalah  menguraikan  emosi  dari  teks  di layanan  sosial  media. 

Studi  penelitian  pendeteksian  emosi telah  menyelidiki  deteksi  emosi  dalam  prosodi,  perubahan keadaan  fisiologis,  ekspresi  wajah  dan  teks. 

Namun,  ada kekurangan  penelitian  dalam  mendeteksi  emosi  dari  teks dibandingkan dengan area lain dari deteksi emosi [2].

Untuk  mendeteksi  emosi  dari  teks  pada  layanan  sosial media twitter dengan data  yang tidak  terstruktur maka  perlu dilakukan  analisis  teks  salah  satunya  dengan  menggunakan Text  Mining.

Text  mining  mencoba  untuk  mengekstrak informasi yang berguna dari sumber data melalui identifikasi dan eksplorasi dari suatu pola menarik.

Sumber  data berupa sekumpulan dokumen dan pola menarik yang tidak ditemukan dalam  bentuk  database record,  tetapi  dalam  data  teks  yang tidak terstruktur.

Beberapa  penelititan  mengenai  deteksi  emosi  telah dilakukan contohnya padap enelitian yang dilakukan Chaitail G. Patil dan Sandip S.Patil  menyebutkan penggunaan metode Support Vector Machine dan dataset ISEAR memiliki akurasi tertinggi  yaitu  71.64%  sedangkan  Metode  Naive  Bayes Classifier akurasinya 60.8% dan yang terendah pada metode Vector Space Model 34.8% dalam untuk Ekstraksi Emosi dari Headline News [3].

Namun Pada penelitianm Arifin and Ketut Eddy  Purnama  melakukan  Klasifikasi  Emosi  Dalam  Teks Bahasa Indonesia menggunakan metode K-Nearest Neighbour.

Pada penelitian yang dilakukan penulis melakukan klasfikasi emosi  pada artikel  yang  ada  diinternet  kemudian dilakukan pengujian  antara  metode  Naïve  Bayes  dengan  K-Nearest Neighbour.

Hasil  dari penelitian tersebut  didapat metode K-Nearest Neighbour menghasilkan nilai akurasi 71.26% yang lebih tinggi daripada metode Naïve Bayes dengan nilai akurasi 58.01% [4].

Berdasarkan  latar  belakang  dan  beberapa  penelitian sebelumnya maka penulis melalui penelitian ini mengusulkan melakukan  penelitian  implementasi text  mining  pada  Sosial Media untuk mendeteksi emosi pengguna.

Metode klasifikasi yang digunakan yaitu metode Support Vector Machine untuk klasifikasi  kelas  emosi  dan  metode  K-Nearest  Neighbour untuk klasifikasi kategori emosi. 

Metode tersebut  digunakan karena metode Support Vector Machine memiliki nilai akurasi tertinggi  pada  penelitian  sebelumnya  serta  Support  Vector Machine  secara  teoritik  dikembangkan  untuk  problem klasifikasi  dengan  dua  class  yang  sangat  tepat  untuk klasifikasi  kelas  emosi[5].

Sedangkan  Metode  K-Nearest Neighbour digunakan  karena pada penelitian sebelumnya K-Nearest  Neighbour  memiliki  akurasi  yang  lebih  tinggi daripada  metode  Naive  Bayes  dan  Metode  K-Nearest Neighbour melakukan  pelatihannya sangat cepat dan  Efektif jika  data  pelatihan  besar  yang  sangat  cocok  dengan penggunaan ISEAR  dataset [6]. 

Deteksi emosi berbasis teks seperti  yang disebutkan sebelumnya dapat  digunakan  dalam bisnis, pendidikan, psikologi, dan bidang lain mana pun yang paling penting untuk memahami dan menafsirkan emosi.


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https://www.researchgate.net/publication/333020467_Text_Mining_pada_Sosial_Media_untuk_Mendeteksi_Emosi_Pengguna_Menggunakan_Metode_Support_Vector_Machine_dan_K-Nearest_Neighbour

Emotion detection of tweets in Indonesian language using LDA and expression symbol conversion

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Abstract:
Twitter is one of the social networks that attract many Indonesian people because it is considered as a medium to express opinions and feelings about certain topic. Twitter popularity can be used as an efficient source of sentiment data for marketing or social studies. Social studies that can be applied to the process of Twitter analysis is emotion detection. Emotion detection has a potency to be applied in a wide range of applications, ranging from health applications, counseling, business, to community population studies. This research utilizes one of the most popular and simplest topic modeling models, that is Latent Dirichlet Allocation (LDA), as well as conversion expression symbol (emoticon/ emoji), which shows the emotion or topic in a tweet to multiply the vocabulary that represents emotion. The advantage of the LDA method proposed is that it can detect some emotions on the tweet because the detection is not rigid and is able to show the proportion of emotion in the tweet. This research compares emotional detection using LDA and conversion expression symbol with emotional detection using LDA without conversion expression symbol. The result shows that emotional detection using LDA with conversion expression symbol is better with the reached average difference of accuracy 14.096%.
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https://ieeexplore.ieee.org/document/8276371

Emotion Analysis Of Twitter Data That Use Emoticons And Emoji Ideograms

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0. Abstract

Twitter is an online social networking service on which users worldwide publish their opinions on a variety of topics, discuss current issues, complain, and express many kinds of emotions. Therefore, Twitter is a rich source of data for opinion mining, sentiment and emotion analysis. This paper focuses on this issue by analysing symbols called emotion tokens, including emotion symbols (e.g. emoticons and emoji ideograms). According to observations, emotion tokens are commonly used in many tweets. They directly express one’s emotions regardless of his/her language, hence they have become a useful signal for sentiment analysis in multilingual tweets. The paper describes the approach to extending existing binary sentiment classification approaches using a multi-way emotions classification.

Keywords: Twitter, data mining, sentiment analysis, emotion analysis.1.

1. Introduction

Microblogging websites such as Twitter (www.twitter.com) have evolved to become a great source of various kinds of information. This is due to the nature of microblogs on which people post real-time messages regarding their opinions on a variety of topics, discuss current issues,complain, and express many kinds of emotions. As the audience of microblogging platforms and social networks grows every day, data from these sources can be used in opinion mining,sentiment and emotion analysis tasks. Opinions and related concepts such as sentiments and emotions are the subjects of study of sentiment analysis and opinion mining. The inception and rapid growth of the field coincide with those of the social media on the Web, e.g., reviews,forum discussions, blogs, microblogs, Twitter, and social networks. Most Natural LanguageProcessing (NLP) methods perform without particular success in the social media. Almost all forms of social media are very noisy and full of all kinds of spelling, grammatical, and punctuation errors.

Current sentiment analysis methods typically focus on the polarity of like/dislike emotions.Sometimes neutral emotions can be detected in between. Human emotions are far beyond these simple metrics and are much more diverse. This implies that such polarity analysis gives only limited information on the actual intent of the author of the message. Defining either positive or negative emotions only is relatively simple, yet defining a complete and clear set of emotions is much more difficult. Researchers have thus created a wide range of research tools on the identification of basic emotions.

2. Related Research

Sentiment analysis is a growing area of the Natural Language Processing task at many levels of granularity. Starting from being a document level classification task ([33], [24]), it has been handled at the sentence level ([16], [18]) and more recently at the phrase level ([34], [6]), or even at the polarity of words and phrases ([15], [12])
However, the informal and specialised language that is used in tweets as well as the nature of the microblogging domain make sentiment analysis in Twitter a very different task. With the growing number of blogs and social networks, opinion mining and sentiment analysis have become fields of interest to many researches. A very broad overview of the existing work was presented in [25]. J. Read, in [27], used emoticons such as “:-)” and “:- (” to form a training set for sentiment classification. For this purpose, the authors collected texts containing emoticons from Usenet newsgroups. The dataset was divided into “positive” (texts with happy emoticons) and “negative” (texts with sad or angry emoticons) samples.
Researchers have also begun to investigate various ways of automatically collecting training data. Several researchers have relied on emoticons to define training data ([23], [9]). Barbosa and Feng in [7] exploited existing Twitter sentiment sites to collect training data. Davidov, Tsur and Rappoport [11] also used hashtags to create training data but they limited their experiments to sentiment/non-sentiment classification rather than the multi-way emotion classification that is presented in this article.
Extending research to many different kinds of emotions is a very new concept and has not been extensively studied yet. There are currently few examples in which researchers have gone beyond the polarity of sentiment analysis. Socher et al. [30] predicted five different dimensions of sentiments. Tromp and Pechenizkiy [32] used Pluchnik’s wheel of emotions model and a rule-based approach for emotion detection in the text. A slightly different but also interesting approach was presented by Mohammad [20], who detected emotion on twitter posts by using emotion-word hashtags.

3. From Sentiment to Emotion Analysis

Sentiment analysis, which is also known as opinion mining, focuses on discovering patterns in the text that can be analysed to classify sentiment in that text. The term sentiment analysis probably first appeared in [21], and the term opinion mining first appeared in [10]. However, research on sentiments and opinions appeared earlier.

According to Liu “sentiment analysis is the field of study that analyses people’s opinions,sentiments, evaluations, appraisals, attitudes, and emotions towards entities such as products,services, organizations, and their attributes. It represents a large problem space. There are also many names and slightly different tasks, e.g., sentiment analysis, opinion mining, opinion extraction, sentiment mining, subjectivity analysis, affect analysis, emotion analysis, review mining, etc.” [19]. Sentiment analysis has grown to be one of the most active research fields in natural language processing. It is also widely studied in data mining, Web mining and text mining. In fact, it has spread from computer science to management sciences and social sciences due to its importance to business and society.

Sentiment analysis is predominantly implemented in software which can autonomously ex-tract emotions and opinions from a text. It has many real-world applications, e.g. it allows companies to analyse how their products or brand is being perceived by their consumers, and politicians may be interested in knowing how people plan to vote in elections, etc. It is difficult to classify sentiment analysis as one specific field of study as it incorporates many different areas, such as linguistics, Natural Language Processing, and Machine Learning or Artificial In-telligence. Since the majority of sentiment that is uploaded to the Internet is of an unstructured nature, it is a difficult task for computers to process it and to extract meaningful information from it. Some of the most effective machine learning algorithms, e.g. support vector machines,naïve Bayes and conditional random fields, often produce no human understandable results.

Emotions are closely related to sentiments. Emotions can be defined as subjective feel-ings and thoughts. People’s emotions have been categorised into distinct categories. Emotionan alysis can thus be done as an additional layer on top of the (relatively) simpler sentiment clas-sification. However, there is still no set of agreed basic emotions among researchers. Based on 26], people have six primary emotions, i.e., love, joy, surprise, anger, sadness, and fear, which can be sub-divided into many secondary and tertiary emotions. Each emotion can also have different intensities.

Emotions in virtual communication differ in a variety of ways from those in face-to-face interactions due to the characteristics of computer-mediated communication, which may lack many of the auditory and visual cues that are normally associated with the emotional aspects of interactions. While text-based communication eliminates audio and visual cues, there are other methods for adding emotion. Emoticons, or emotional icons, can be used to display various types of emotions. Ortony and Turner [22] collated a wide range of research on the identification of basic emotions (Table 1)
 

There are many common categories in the different research studies as presented in Table 1,but there are still too many very different emotions for effective analysis. Some concepts aimto minimise the number of basic emotions. Jack, Garrod and Schyns [17] analysed the 42 facialmuscles that shape emotions on the face and they came up with only four basic emotions, yetthere is still no consensus on the basic set of emotions that would be generally accepted andcould be objectively verified. For the purposes of this work, emotions can be classified intoemoticon types similar to those in Wikipedia [5] (Table 2):
 

4. Twitter Data Description

Twitter has its own conventions that renders it distinct from other textual data; Twitter messages are called tweets. Twitter also has its own conventions that renders it distinct from other textual data. There are some particular features that can be used to compose a tweet 1.The first pieces of information, UE Katowice and @UE_Katowice are the twitter name for the University of Economics in Katowice; #UEKatowice, #international, and #Erasmus are tags provided by the user for this message, the so-called hashtags. Users of Twitter use the “@” symbol to refer to other users. Referring to other users in this manner automatically alerts them.Users usually use hashtags to mark topics. This is primarily done to increase the visibility of their tweets. These symbols provide an easy way of identifying Twitter user names and topics and thus allows to search for and filter information on any subject. In the tweet the following emoticons, – :), and emoji characters — were used.

Twitter messages have many unique attributes, which differentiates twitter analysis from other fields of research. The first attribute is length. The maximum length of a Twitter message is 140 characters. The average length of a tweet is 14 words [14]. This is very different from the domains of other research studies, which were mostly focused on reviews which consisted of multiple sentences. The second attribute is the availability of data. With the Twitter API or other tools it is much easier to collect millions of tweets for training.

4.1. Emoticons

There are two fundamental data mining tasks that can be considered in conjunction with Twitter data, i.e. text analysis and symbol analysis. Due to the nature of this microblogging service(quick and short messages), people use acronyms, make spelling mistakes, and use emoticons and other characters that express special meanings. Emoticons constitute a meta communicative pictorial representation of a facial expression that is pictorially represented by using punctuation and letters or pictures; they express the user’s mood.The use of emoticons can be traced back to the 19th century. The first documented person to have used the emoticons :-) and :-( on the Internet was Scott Fahlman from Carnegie Mellon University in a message dated 19 September 1982 [1].Some emoticons as characters are included in the Unicode standard, three in the Miscellaneous Symbols block, and over sixty in the Emoticons block [4]. More symbols and meanings which can be used to determine emotional state can be found on the Wikipedia website [5]. Thetop 20 emoticons collected from 96 269 892 tweets is presented in [8].

4.2. Emoji ideograms

Emoji were originally used in Japanese electronic messages and spread outside of Japan. The characters are used much like emoticons, although a wider range is provided. The rise in the popularity of emoji is due to its being incorporated into sets of characters available in mo-bile phones. Apple in IOS, Android and other mobile operating systems included some emoji character sets. Emoji characters are also included in the Unicode standard [4]. Emoji can be categorised into similar categories as emoticons. Emoji can even be translated into English by using http://emojitranslate.com website.

5. Architecture of Emotion Classification System

The main problem is how to extract the rich information that is available on Twitter and how to use it to draw meaningful insight. To achieve this, at first stage of works an accurate analyser for tweets was build. Twitter allows developers to collect data via Twitter REST API [2] and The Streaming API [3]. Twitter has numerous regulations and rate limits imposed on its API, and for this reason it requires that all users must register an account and provide authentication details when they query the API. One of the best ways of connecting to Twitter Streaming API and downloading the data is by using Python and a library called Tweepy [28].

Twitter API exports data only in JSON format, which should be translated into readable for databases or an analytical software format. A combination of Twitter API, scripts for converting JSON to CSV [29], SAS Macro [13] or Excel Macro [31] was used to extract information from Twitter and to create an input dataset for the analysis. The entire process of data acquisition can be fully automated by scheduling the run of VBA or SAS macros.

Data can also be stored directly in JSON format in an NoSQL Database, which provides a mechanism for the storage and retrieval of data which is modelled in means other than the tabular relations used in relational databases. MongoDB (https://www.mongodb.org/)allows to store data in JSON-like documents with dynamic schemas (MongoDB calls the format BSON), thus making the integration of data in certain types of applications easier and faster.

Since opinions have targets, further preprocessing and filtering of collected data was done using @twitter_names and #hashtags as targets in the way described in [2]. This method is more precise and provides better results than other text mining approaches. Software used for data analysis can be SAS Text Miner, SAS Visual Analytics or other tools. SAS Visual Analytics allows for direct import of Twitter data, but in order to use SAS Text Miner and other tools, the data have to be downloaded and converted.

Instead of using commercial software, all sentiment and emotion analysis tasks was solved using python programming language. Python language has many machine learning and data mining extensions that are suitable for this kind of work. The challenge remains to fetch customised Tweets and to clean data before any text or symbol mining takes place.

A classification of tweets was made in unsupervised learning method by using the lexicon-based approach. The sentiment lexicon contains a list of emoticons and emoji ideograms based on Table 2. Data was gathered by searching Twitter posts using Twitter API. An assumption must be made in order to use this method, this assumption is that the emoticon in the tweet represents the overall emotion contained in that tweet. This assumption is quite reasonable as the maximum length of a tweet is 140 characters, so in the majority of cases the emoticon will correctly represent the overall sentiment of that tweet. This kind of evaluation is commonly known as the document-level sentiment classification because it considers the whole document as a basic information unit. The model can be developed on a sample of data; then can be used to classify the emotions of the tweet.

First verification of the method used here indicates that the most recognisable are only the basic emotions. The obtained results are similar to work [8], in which Nick Berry reveals that the top 20 emoticons accounted for 90% of all 96,269,892 Tweets. Therefore, for effective emotion analysis we could use only a limited subset of emoticons and recognised only basic, common emotions, such as:

This set of emoticons and the corresponding emotions covers nearly 90% of all occurrences.

However objective verification of emotions is very difficult. Further work should go in the direction of carrying out supervised check what part of emoticons does not reflect correctly emotions of tweet.

6. Conclusions

Microblogging such as on Twitter has today become one of the major types of communication.The large amount of information contained in these websites makes them an attractive source of data for opinion mining and sentiment analysis. Most text-based methods of analysis maynot always be useful for sentiment analysis in these domains. We as researchers still need novel ideas to make significant progress in this area. Using symbol analysis that makes use of emoticons and emoji characters can significantly increase precision in recognising many kinds of emotions. Also, applying Twitter names and hashtags to filter collected training data can provide better results. The most successful algorithms will probably be integration of natural language processing methods and symbol analysis
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References 

1. :-) turns 25. https://web.archive.org/web/20071012051803/http://www.cnn.com/2007/TECH/09/18/emoticon.anniversary.ap/index.html, 09 2007.
2. Twitter rest api. the search api. https://dev.twitter.com/rest/public/search, 05 2015.
3. Twitter the streaming apis. https://dev.twitter.com/streaming/overview, 05 2015.
4. Unicode miscellaneous symbols. http://www.unicode.org/charts/PDF/U2600.pdf, 05 2015.
5. List of emoticons. https://en.wikipedia.org/wiki/List_of_emoticons, 04 2016.
6. Apoorv Agarwal, Fadi Biadsy, and Kathleen R. Mckeown. Contextual phrase-levelpolarity analysis using lexical affect scoring and syntactic n-grams. In Proceedingsof the 12th Conference of the European Chapter of the Association for ComputationalLinguistics, EACL ’09, pages 24–32, Stroudsburg, PA, USA, 2009. Association forComputational Linguistics.
7. Luciano Barbosa and Junlan Feng. Robust sentiment detection on twitter from biasedand noisy data. In Proceedings of the 23rd International Conference on ComputationalLinguistics: Posters, COLING ’10, pages 36–44, Stroudsburg, PA, USA, 2010. Associ-ation for Computational Linguistics.
8. N. Berry. Datagenetics. http://www.datagenetics.com/blog/october52012/index.html, 05 2015.
9. Albert Bifet and Eibe Frank. Sentiment knowledge discovery in twitter streaming data.In Proceedings of the 13th International Conference on Discovery Science, DS’10,pages 1–15, Berlin, Heidelberg, 2010. Springer-Verlag.
10. Kushal Dave, Steve Lawrence, and David M. Pennock. Mining the peanut gallery:Opinion extraction and semantic classification of product reviews. In Proceedings ofthe 12th International Conference on World Wide Web, WWW ’03, pages 519–528,New York, NY, USA, 2003. ACM.
11. Dmitry Davidov, Oren Tsur, and Ari Rappoport. Enhanced sentiment learning usingtwitter hashtags and smileys. In Proceedings of the 23rd International Conference onComputational Linguistics: Posters, COLING ’10, pages 241–249, Stroudsburg, PA,USA, 2010. Association for Computational Linguistics.
12. Andrea Esuli and Fabrizio Sebastiani. Sentiwordnet: A publicly available lexical re-source for opinion mining. In In Proceedings of the 5th Conference on Language Re-sources and Evaluation (LREC’06, pages 417–422, 2006.
13. S. Garla and G. Chakraborty. tweets. Paper 324-2011. Oklahoma State University,Stillwater, OK, 2001.
14. Alec Go, Richa Bhayani, and Lei Huang. Twitter sentiment classification using distant supervision. Processing, pages 1–6, 2009.
15. Vasileios Hatzivassiloglou and Kathleen R. McKeown. Predicting the semantic orien-tation of adjectives. In Proceedings of the 35th Annual Meeting of the Association forComputational Linguistics and Eighth Conference of the European Chapter of the As-sociation for Computational Linguistics, ACL ’98, pages 174–181, Stroudsburg, PA,USA, 1997. Association for Computational Linguistics.
16. Minqing Hu and Bing Liu. Mining and summarizing customer reviews. In Proceedingsof the Tenth ACM SIGKDD International Conference on Knowledge Discovery andData Mining, KDD ’04, pages 168–177, New York, NY, USA, 2004. ACM.
17. Rachael E. Jack, Oliver G.B. Garrod, and Philippe G. Schyns. Dynamic facial expres-sions of emotion transmit an evolving hierarchy of signals over time. Current Biology,24(2):187–192, 2014.
18. Soo-Min Kim and Eduard Hovy. Determining the sentiment of opinions. In Proceed-ings of the 20th International Conference on Computational Linguistics, COLING ’04,Stroudsburg, PA, USA, 2004. Association for Computational Linguistics.
19. Bing Liu. Sentiment analysis and opinion mining. Synthesis Lectures on Human Lan-guage Technologies, 5(1):1–167, 2012.
20. Saif Mohammad. #emotional tweets. In *SEM 2012: The First Joint Conference onLexical and Computational Semantics – Volume 1: Proceedings of the main conferenceand the shared task, and Volume 2: Proceedings of the Sixth International Workshopon Semantic Evaluation (SemEval 2012), pages 246–255, Montréal, Canada, 7-8 June2012. Association for Computational Linguistics.
21. Tetsuya Nasukawa and Jeonghee Yi. Sentiment analysis: Capturing favorability usingnatural language processing. In Proceedings of the 2Nd International Conference onKnowledge Capture, K-CAP ’03, pages 70–77, New York, NY, USA, 2003. ACM.
22. Andrew Ortony and Terence J Turner. What’s basic about basic emotions? Psycholog-ical review, 97(3):315, 1990.
23. Alexander Pak and Patrick Paroubek. Twitter as a corpus for sentiment analysis andopinion mining. In Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Mae-gaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Mike Rosner, and Daniel Tapias,editors, Proceedings of the Seventh International Conference on Language Resourcesand Evaluation (LREC’10), Valletta, Malta, may 2010. European Language ResourcesAssociation (ELRA).
24. Bo Pang and Lillian Lee. A sentimental education: Sentiment analysis using subjectiv-ity summarization based on minimum cuts. In Proceedings of the 42Nd Annual Meetingon Association for Computational Linguistics, ACL ’04, Stroudsburg, PA, USA, 2004.Association for Computational Linguistics.
25. Bo Pang and Lillian Lee. Opinion mining and sentiment analysis. Found. Trends Inf.Retr., 2(1-2):1–135, January 2008.
26. W Parrott. Emotions in social psychology: Essential readings. Psychology Press, 2001.
27. Jonathon Read. Using emoticons to reduce dependency in machine learning techniquesfor sentiment classification. In Proceedings of the ACL Student Research Workshop,ACLstudent ’05, pages 43–48, Stroudsburg, PA, USA, 2005. Association for Computa-tional Linguistics.
28. Matthew A. Russell. Mining the Social Web, 2nd Edition Data Mining Facebook, Twit-ter, LinkedIn, Google+, GitHub, and More. O’Reilly Media, 2013.
29. Falko Shultz. How to import twitter tweets in sas data step using oauth 2 authenti-cation style. http://blogs.sas.com/content/sascom/2013/12/12/how-to-import-twitter- tweets-in-sas-data-step- using-oauth/-2-authentication-style/, 12 2013.
30. Richard Socher, Jeffrey Pennington, Eric H. Huang, Andrew Y. Ng, and Christopher D.Manning. Semi-supervised recursive autoencoders for predicting sentiment distribu-tions. In Proceedings of the Conference on Empirical Methods in Natural LanguageProcessing, EMNLP ’11, pages 151–161, Stroudsburg, PA, USA, 2011. Associationfor Computational Linguistics.
31. Analytics Tools. Can sas be used to gather sentiments fromtwitter? http://www.analytics-tools.com/2012/06/social-media-analytics-twitter- text.html, 07 2012.
32. Erik Tromp and Mykola Pechenizkiy. Pattern-based emotion classification on socialmedia. In Advances in Social Media Analysis, volume 602, pages 1–20. Springer, 2015.
33. Peter D. Turney. Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Annual Meeting onAssociation for Computational Linguistics, ACL ’02, pages 417–424, Stroudsburg, PA,USA, 2002. Association for Computational Linguistics.
34. Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. Recognizing contextual polarity inphrase-level sentiment analysis. In Proceedings of the Conference on Human LanguageTechnology and Empirical Methods in Natural Language Processing, HLT ’05, pages347–354, Stroudsburg, PA, USA, 2005. Association for Computational Linguistics.

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https://www.researchgate.net/publication/308413240_TWITTER_SENTIMENT_ANALYSIS_USING_EMOTICONS_AND_EMOJI_IDEOGRAMS

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