Explore the Effects of Emoticons on Twitter Sentiment Analysis

Węgrzyn-Wolska, Katarzyna; Bougueroua, Lamine; Yu, Hwanjo; Zhong, Jianxin

doi:10.5121/csit.2016.61006

Cited by 10 publications

(18 citation statements)

References 9 publications

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“…The research in [20] shows that the importance of emoticons on polarity sentiment classification especially in social networks is undeniable and their popularity is getting higher and higher.…”

Section: Effects Of Emoticons Removing Punctuations and Urlsmentioning

confidence: 99%

The impact of text preprocessing on the prediction of review ratings

Işik¹,

Dağ²

2020

Turk J Elec Eng & Comp Sci

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With the increase of e-commerce platforms and online applications, businessmen are looking to have a rating and review system through which they can easily reveal the feelings of customers related to their products and services. It is undeniable from the statistics that online ratings and reviews attract new customers as well as increase sales by means of providing confidence, ratification, opinions, comparisons, merchant credibility, etc. Although considerable research has been devoted to the sentiment analysis for review classification, rather less attention has been paid to the text preprocessing which is a crucial step in opinion mining especially if convenient preprocessing strategies are found out to increase the classification accuracy. In this paper, we concentrate on the impact of simple text preprocessing decisions in order to predict fine-grained review rating stars whereas the majority of previous work focused on the binary distinction of positive vs. negative. Therefore, the aim of this research is to analyze preprocessing techniques and their influence, at the same time explain the interesting observations and results on the performance of a five-class-based review rating classifier.

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Section: Effects Of Emoticons Removing Punctuations and Urlsmentioning

confidence: 99%

The impact of text preprocessing on the prediction of review ratings

Işik¹,

Dağ²

2020

Turk J Elec Eng & Comp Sci

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“…One can't directly remove meaningful punctuations during preprocessing, because meaningful punctuation (Emoticons) convey opinion towards target entities. Wegrzyn-Wolska, K et al [26] compared three emoticon's preprocessing methods; Emotion deletion (emodel), emoticons 2-values translation (emo2label) and emoticon explanation (emo2explanation). Emoticon weight lexicon was used with Naïve Bayes Classier in order to assess the effect of emotion icons.…”

Section: Related Workmentioning

confidence: 99%

Normalization of Unstructured and Informal Text in Sentiment Analysis

Javed¹,

Kamal²

2018

ijacsa

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Sentiment Analysis is problem of natural language processing which deals with the extraction and analysis of public sentiments shared about target entities over microbloging websites. This field has gained great attention due to the huge availability of decision making textual contents. Sentiment Analysis has enormous application areas such as; Market Analysis, Service Analysis, Showbiz analysis, Movies, sports and even the popularity and acceptance rate of political policies can also be predicted via sentiment analysis systems. Although tremendous volume of opinionative text is available but it is unstructured and noisy due to which sentiment classifiers can't achieve good outcomes. Normalization is the process used to clean noise from unstructured text for sentiment analysis. In this study we have proposed a mechanism for the normalization of informal and unstructured text. Proposed mechanism is comprised of four essential phases; Noise Reduction, Part of Speech Tagging, Stop Word Removal stemming and Lemmatization. Numerous experiments are performed on twitter data set with unsupervised lexicons and dictionaries. Python and Natural language toolkit is used for performing all four essential steps. This study demonstrates that utilization and normalization of informal tokens in tweets improved the overall classification accuracy from 75.42 to 82.357.

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“…Emoticons are ways of communicating feelings and even adding textual information to a social network [21]. However, few studies have explored the relationship between emoticons and textual information [16], [18], and even fewer to the relationship between emoticons and the expression of feelings or sentiments [17]. While it is claimed that emoticons and, most likely, reactions, can be used to express users' emotions, there have still been few studies that investigate whether they actually reflect them.…”

Section: Introductionmentioning

confidence: 99%

How Do I Feel? Identifying Emotional Expressions on Facebook Reactions Using Clustering Mechanism

et al. 2019

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The recognition of emotions and feelings through computer technology and devices has been widely explored in recent years. Social networks have become a natural environment in which users express their feelings and opinions through social media, and this includes their Facebook reactions. The aim of this study was to investigate whether the emoticons have chosen by users in social network news actually express the emotions they wish to express, having as indicative, the polarity of the emotions, and the six basic emotions. The data collection was carried out following three courses of action: 1) survey of the posts with higher reactions rates of popular news pages; 2) selection of news by a panel of experts to verify its reliability; and 3) identification of reactions, polarity, and basic emotions flagged by Facebook users for each news item. Finally, an Expectation-Maximization algorithm was deployed to find the relationship between the reactions and the basic emotions signaled. The results made it possible to determine the polarity and the correlation of the reactions with the emotional expressions. This suggests that the use of reactions in feelings analysis algorithms can increase the confidence in determining the emotion that the content reflects and the emotional state of the social network users.

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Explore the Effects of Emoticons on Twitter Sentiment Analysis

Abstract: ABSTRACT

Cited by 10 publications

References 9 publications

The impact of text preprocessing on the prediction of review ratings

The impact of text preprocessing on the prediction of review ratings

Normalization of Unstructured and Informal Text in Sentiment Analysis

How Do I Feel? Identifying Emotional Expressions on Facebook Reactions Using Clustering Mechanism

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