A Novel Approach for Polarity Determination Using Emoticons: Emoticon-Graph
01 Jan 2016-pp 481-489
TL;DR: This paper proposes a system to account for emoticons and exclamation marks along with words while performing sentiment analysis of the input text, and indicates that dynamically plotted emoticon-graphs could play a major role in simplifying the results of polarity determination methods.
Abstract: Owing to the rising popularity of social networking sites and chat-based applications, visual sentiment clues such as emoticons are increasingly being used in blogs, tweets, games, and product reviews. The existing sentiment analysis tools mainly focus on predicting the polarity based on textual content, and displaying the results in the form of graphs or charts. In this paper, we propose a system to account for emoticons and exclamation marks along with words while performing sentiment analysis of the input text. The output of this analysis is represented on a unique figure, which we define as an ‘emoticon-graph’. An online survey was conducted to collect product and news reviews to analyze the sentiment and also to evaluate the acceptance of the ‘emoticon-graph’. The findings of this survey indicate that dynamically plotted emoticon-graphs could play a major role in simplifying the results of polarity determination methods.
Citations
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TL;DR: This survey article covers the comprehensive overview of the last update in this field and includes the sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in the MSA and its related areas.
Abstract: Multimodal sentiments have become the challenge for the researchers and are equally sophisticated for an appliance to understand. One of the studies that support MS problems is a MSA, which is the training of emotions, attitude, and opinion from the audiovisual format. This survey article covers the comprehensive overview of the last update in this field. Many recently proposed algorithms and various MSA applications are presented briefly in this survey. The article is categorized according to their contributions in the various MSA techniques. The main purpose of this survey is to provide a full image of the MSA opportunities and difficulties and related field with brief details. The main contribution of this article includes the sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in the MSA and its related areas.
61 citations
01 Feb 2017
TL;DR: Various factors that affect sentiment analysis are discussed briefly in this paper and various issues like sarcasm detection, multilingualism, handling acronyms and slang language, lexical variation and dynamic dictionary handling are discussed.
Abstract: Social networking has gradually become a routine for people to post their opinions, views and comments on any product or person. People share their feelings online in a very informal language. Thus, it is very difficult task to analyze exact sentiments attached with that natural language. Sentiment Analysis is a study of people's attitude, opinions, and emotions to classify whether it is positive, negative or neutral. Use of emoticons on social media has increased rapidly in recent years. Hence, we have focused more on how emoticons play an important role in sentiment analysis. Various factors that affect sentiment analysis are discussed briefly in this paper. Also various issues like sarcasm detection, multilingualism, handling acronyms and slang language, lexical variation and dynamic dictionary handling are discussed.
25 citations
Cites background or methods from "A Novel Approach for Polarity Deter..."
...Whereas dictionary based approach is used by combining existing seeds present in the dictionary to build more dictionaries [1]....
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...They are called “Amplifiers” and add more sentiments to words associated with them [1]....
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...A novel Approach for Polarity Determination Using Emoticons: Emoticon-Graph [1] Online Survey on application reviewing Blank space and punctuation removal Dictionary based Lexicon Approach...
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...Summary of existing Techniques Referred work Data Sources Pre-Processing Technique used A novel Approach for Polarity Determination Using Emoticons: Emoticon-Graph [1] Online Survey on application reviewing Blank space and punctuation removal Dictionary based Lexicon Approach Localized Twitter Opinion Mining using Sentiment Analysis [2] Twitter Oauth POS Tagging Corpus based Lexicon Approach Exploiting Emoticons in Sentiment Analysis [3] Dutch Tweets Segmentation Dictionary based Lexicon Approach MoodLens: An Emoticon-Based Sentiment Analysis System for Chinese Tweets [4] Weibo Tweets Bag of Word Incremental Naïve Bayes Classifier Monitoring System for Potential Users with Depression Using Sentiment Analysis [5] Social Network Posts Filtering using Machine Learning (SVM) Corpus based Lexicon Approach A...
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10 Jun 2022
TL;DR: In this paper , the authors present a comprehensive overview of the last update in the field of multimodal sentiment analysis (MSA), which is the training of emotions, attitude, and opinion from audiovisual format.
Abstract: Multimodal sentiments have become the challenge for the researchers and are equally sophisticated for an appliance to understand. One of the studies that support MS problems is a MSA, which is the training of emotions, attitude, and opinion from the audiovisual format. This survey article covers the comprehensive overview of the last update in this field. Many recently proposed algorithms and various MSA applications are presented briefly in this survey. The article is categorized according to their contributions in the various MSA techniques. The main purpose of this survey is to provide a full image of the MSA opportunities and difficulties and related field with brief details. The main contribution of this article includes the sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in the MSA and its related areas.
14 citations
References
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TL;DR: The results indicate that emoticons’ contributions were outweighed by verbal content, but a negativity effect appeared such that any negative message aspect—verbal or graphic—shifts message interpretation in the direction of the negative element.
Abstract: Emoticons are graphic representations of facial expressions that many e-mail users embed in their messages. These symbols are widely known and commonly recognized among computer-mediated communication (CMC) users, and they are described by most observers as substituting for the nonverbal cues that are missing from CMC in comparison to face-to-face communication. Their empirical impacts, however, are undocumented. An experiment sought to determine the effects of three common emoticons on message interpretations. Hypotheses drawn from literature on nonverbal communication reflect several plausible relationships between emoticons and verbal messages. The results indicate that emoticons’ contributions were outweighed by verbal content, but a negativity effect appeared such that any negative message aspect—verbal or graphic—shifts message interpretation in the direction of the negative element.
758 citations
12 Aug 2012
TL;DR: MoodLens is the first system for sentiment analysis of Chinese tweets in Weibo, and by using the highly efficient Naive Bayes classifier, MoodLens is capable of online real-time sentiment monitoring.
Abstract: Recent years have witnessed the explosive growth of online social media. Weibo, a Twitter-like online social network in China, has attracted more than 300 million users in less than three years, with more than 1000 tweets generated in every second. These tweets not only convey the factual information, but also reflect the emotional states of the authors, which are very important for understanding user behaviors. However, a tweet in Weibo is extremely short and the words it contains evolve extraordinarily fast. Moreover, the Chinese corpus of sentiments is still very small, which prevents the conventional keyword-based methods from being used. In light of this, we build a system called MoodLens, which to our best knowledge is the first system for sentiment analysis of Chinese tweets in Weibo. In MoodLens, 95 emoticons are mapped into four categories of sentiments, i.e. angry, disgusting, joyful, and sad, which serve as the class labels of tweets. We then collect over 3.5 million labeled tweets as the corpus and train a fast Naive Bayes classifier, with an empirical precision of 64.3%. MoodLens also implements an incremental learning method to tackle the problem of the sentiment shift and the generation of new words. Using MoodLens for real-time tweets obtained from Weibo, several interesting temporal and spatial patterns are observed. Also, sentiment variations are well captured by MoodLens to effectively detect abnormal events in China. Finally, by using the highly efficient Naive Bayes classifier, MoodLens is capable of online real-time sentiment monitoring. The demo of MoodLens can be found at http://goo.gl/8DQ65.
261 citations
18 Mar 2013
TL;DR: How emoticons typically convey sentiment is analyzed and how to exploit this by using a novel, manually created emoticon sentiment lexicon in order to improve a state-of-the-art lexicon-based sentiment classification method.
Abstract: As people increasingly use emoticons in text in order to express, stress, or disambiguate their sentiment, it is crucial for automated sentiment analysis tools to correctly account for such graphical cues for sentiment. We analyze how emoticons typically convey sentiment and demonstrate how we can exploit this by using a novel, manually created emoticon sentiment lexicon in order to improve a state-of-the-art lexicon-based sentiment classification method. We evaluate our approach on 2,080 Dutch tweets and forum messages, which all contain emoticons and have been manually annotated for sentiment. On this corpus, paragraph-level accounting for sentiment implied by emoticons significantly improves sentiment classification accuracy. This indicates that whenever emoticons are used, their associated sentiment dominates the sentiment conveyed by textual cues and forms a good proxy for intended sentiment.
201 citations
TL;DR: The characteristics of the socio-cognitive processes-emotional expression, context definition, and identity creation-used by the interlocutors to make order and create relationships out of the miscommunication processes typical of CMC are described.
Abstract: The increased diffusion of the Internet has made computer-mediated communication (CMC) very popular. However, a difficult question arises for psychologists and communication researchers: "What are the communicative characteristics of CMC?" According to the "cuesfiltered-out" approach, CMC lacks the specifically relational features (social cues), which enable the interlocutors to identify correctly the kind of interpersonal situations they find themselves in. This paper counters this vision by integrating in its theoretical frame the different psycho-social approaches available in current literature. In particular, the paper describes the characteristics of the socio-cognitive processes—emotional expression, context definition, and identity creation—used by the interlocutors to make order and create relationships out of the miscommunication processes typical of CMC. Moreover, it presents the emerging forms of CMC—instant messaging, shared hypermedia, weblogs, and graphical chats—and their possible social a...
186 citations
TL;DR: The evaluation of CAO confirmed the system's capability to sufficiently detect and extract any emoticon, analyze its semantic structure, and estimate the potential emotion types expressed, outperforming existing emoticon analysis systems.
Abstract: This paper presents CAO, a system for affect analysis of emoticons in Japanese online communication. Emoticons are strings of symbols widely used in text-based online communication to convey user emotions. The presented system extracts emoticons from input and determines the specific emotion types they express with a three-step procedure. First, it matches the extracted emoticons to a predetermined raw emoticon database. The database contains over 10,000 emoticon samples extracted from the Web and annotated automatically. The emoticons for which emotion types could not be determined using only this database, are automatically divided into semantic areas representing “mouths” or “eyes,” based on the idea of kinemes from the theory of kinesics. The areas are automatically annotated according to their co-occurrence in the database. The annotation is first based on the eye-mouth-eye triplet, and if no such triplet is found, all semantic areas are estimated separately. This provides hints about potential groups of expressed emotions, giving the system coverage exceeding 3 million possibilities. The evaluation, performed on both training and test sets, confirmed the system's capability to sufficiently detect and extract any emoticon, analyze its semantic structure, and estimate the potential emotion types expressed. The system achieved nearly ideal scores, outperforming existing emoticon analysis systems.
55 citations