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Journal ArticleDOI

Text-based sentiment analysis: review

V. P. Lijo1, Hari Seetha1
01 Dec 2017-International Journal of Knowledge and Learning (Inderscience Publishers (IEL))-Vol. 12, Iss: 1, pp 1-26
TL;DR: This paper presents a review covering techniques, tools, data resources and applications in the area of text-based sentiment analysis (SA) and opinion mining.
Abstract: The impact of the social networks-based sentiment analysis (SA) and opinion mining has increased in recent times. Decision-makers consider the opinions of the thought leaders and laymen, and plenty of opinions are available in social networks. When a user wants to get a service or buy a product he or she will check for the reviews and opinions provided by other people about various offerings. Opinion rich data sources are available in digital form; this attracts many researchers to focus research on SA. The 'sentiments' available in social networks and review pages are highly valuable for industries and individuals who want to closely monitor their reputation and live feedback about their services and products. This paper presents a review covering techniques, tools, data resources and applications in the area of text-based SA.
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Book ChapterDOI
07 Jun 2019
TL;DR: The sequential minimal optimization with MapReduce (SMOMR) is used to achieve enhanced efficiency as well as scalability and the experiment results reveal that this method outperforms many existing methods.
Abstract: Sentiment analysis is an interesting area of research due to the availability of sentiment data and opinion-oriented services. The efficiency and scalability of the sentiment analysis applications are important concerns as they expect accurate results in short period of time by processing a large amount of data. An efficient and scalable polarity detection method is proposed in this paper. The sequential minimal optimization with MapReduce (SMOMR) is used to achieve enhanced efficiency as well as scalability. The experiment results reveal that this method outperforms many existing methods.
Journal ArticleDOI
TL;DR: In this article, the authors present the perception of 611 students from public universities in Mexico about online classes in pandemic times and propose a framework for considering the emotional aspects of positive engagement in student learning online.
Abstract: This article presents the perception of 611 students from public universities in Mexico about online classes in pandemic times. Exploratory factor analysis was conducted. Vygotsky’s contributions are taken, especially those related to emotional manifestations. It is concluded that the most important factor in this change is the emotions that are identified as an impulse to act during online classes, given the didactic and pedagogical components, resources, and supports for students that promote learning. This proposes a framework for considering the emotional aspects of positive engagement in student learning online. There is evidence of some balance between the advantages and disadvantages of online education. It also suggests that women perceive more negative emotions such as disappointment and frustration in the online education experience. The teacher, being a vicarious learning model, can innovate in education by promoting peripheral participation with other playful activities that involve , applied, and situated learning. Copyright © 2022, IGI Global.
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Book ChapterDOI
01 Jan 2017
TL;DR: This chapter describes the sentiment strength detection program SentiStrength that was developed during the CyberEmotions project to detect the strength of sentiments expressed in social web texts.
Abstract: Emotions are important in communication to effectively convey messages and to understand reactions to messages. Large scale studies of communication need methods to detect sentiment in order to investigate or model the processes involved. This chapter describes the sentiment strength detection program SentiStrength that was developed during the CyberEmotions project to detect the strength of sentiments expressed in social web texts. SentiStrength uses a lexical approach that exploits a list of sentiment-related terms and has rules to deal with standard linguistic and social web methods to express sentiment, such as emoticons, exaggerated punctuation and deliberate misspellings. This chapter also describes how SentiStrength can be refined for particular topics and contexts and how variants can be created for different languages. The chapter also briefly describes some studies that have applied SentiStrength to analyse trends in Twitter and You Tube comments.

170 citations