An Improved Sentiment Classification using Lexicon into SVM

Rastogi, S. S. K.; Singhal, Rohit Kumar; Kumar, Anil

doi:10.5120/16562-6226

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…The corpus construction process from [22] is adapted in this research. It contains corpus filtering, web search, and filtering using linguistic patterns and domain-specific polarity lexicon.…”

Section: Corpus Constructionmentioning

confidence: 99%

Multistage Sentiment Classification Model using Malaysia Political Ontology

Ismail¹,

Abdullah²,

Idrus³

2021

IJACSA

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Section: Corpus Constructionmentioning

confidence: 99%

Multistage Sentiment Classification Model using Malaysia Political Ontology

Ismail¹,

Abdullah²,

Idrus³

2021

IJACSA

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“…Machine learning generates better results in maximum cases compared with the lexicon-based approach. SVM approaches using sentiment lexicons improve the accuracy of sentiment analysis and also create domain-specific sentiment lexicons for learning purposes [48][49][50]. Sentiment bias processing and multiple clustered-based support vector machine classifiers applied to the lexicon-based sentiment analysis method where a sentiment scoring formula is used to classify the reviews is considered to improve the performance of lexicon-based review [51][52][53].…”

Section: Sentiment Analysis and Lexicon Based Svm Classificationmentioning

confidence: 99%

A Recommendation Mechanism for Under-Emphasized Tourist Spots Using Topic Modeling and Sentiment Analysis

Shafqat

Byun

2019

Sustainability

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With rapid advancements in internet applications, the growth rate of recommendation systems for tourists has skyrocketed. This has generated an enormous amount of travel-based data in the form of reviews, blogs, and ratings. However, most recommendation systems only recommend the top-rated places. Along with the top-ranked places, we aim to discover places that are often ignored by tourists owing to lack of promotion or effective advertising, referred to as under-emphasized locations. In this study, we use all relevant data, such as travel blogs, ratings, and reviews, in order to obtain optimal recommendations. We also aim to discover the latent factors that need to be addressed, such as food, cleanliness, and opening hours, and recommend a tourist place based on user history data. In this study, we propose a cross mapping table approach based on the location’s popularity, ratings, latent topics, and sentiments. An objective function for recommendation optimization is formulated based on these mappings. The baseline algorithms are latent Dirichlet allocation (LDA) and support vector machine (SVM). Our results show that the combined features of LDA, SVM, ratings, and cross mappings are conducive to enhanced performance. The main motivation of this study was to help tourist industries to direct more attention towards designing effective promotional activities for under-emphasized locations.

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“…However, product and service opinions voiced in other social media formats, such as Twitter and Facebook, do not include such global rating scales. These verbatim messages also differ in the strength of the conviction expressed through the forcefulness of the language being used (Rastogi et al, 2014). Speech act theory again may provide insights, in the form of a theoretically rooted understanding of how to derive the strength of writers' intentions from the meaning of the sentence in which they appear (Searle, 1969).…”

Section: Decoding Strength Of Convictionmentioning

confidence: 99%

Decoding social media speak: developing a speech act theory research agenda

Ludwig

Ruyter

2016

Journal of Consumer Marketing

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Purpose-Drawing on the theoretical domain of speech act theory and a discussion of its suitability for setting the agenda for social media research, this study explores a range of research directions that are both relevant and conceptually robust, to stimulate the advancement of knowledge and understanding of online verbatim data. Permanent repository link:Design/methodology/approach-Examining previously published cross-disciplinary research, the study identifies how recent conceptual and empirical advances in speech act theory may further guide the development of text analytics in a social media context.Findings-Decoding content and function word use in customers' social media communication can enhance the efficiency of determining (1) potential impacts of customer reviews, (2) sentiment strength, (3) the quality of contributions in social media, (4) customers' socialization perceptions in online communities, and (5) deceptive messages.Originality/value-Considering the variety of managerial demand, increasing and diverging social media formats, expanding archives, rapid development of software tools, and fastpaced market changes, this study provides an urgently needed, theory-driven, coherent 2 research agenda to guide the conceptual development of text analytics in a social media context. Keywords-Social media communication, Text mining, Speech act theoryPaper type-Conceptual paper "Language is the currency of most human social processes." (Chung and Pennebaker, 2007 p.343) The past decade has witnessed an incredible increase in the volume and importance of online social media conversations. Spanning virtually all ages, ethnicities, and geographies, the spectacular rise of websites such as Facebook, LinkedIn, and Twitter is revolutionizing the way people communicate and interact with friends, family, colleagues, complete strangers, and businesses. The increasing prevalence and accessibility of social media communication and online discussions, together with their powerful influence on purchase decisions and product/service evaluations, have become central driving forces for marketing decision makers (Bharadwaj et al., 2013). Sixty percent of Facebook users comment directly on the products and service they purchase (Incite, 2011), and 72% of consumers expect brands to respond within an hour to complaints posted on Twitter (Gesenhus 2013). Business performance in turn are suffering, as a result of companies' inability to decipher and analyse conversations with and among their online customers (Bonnet and Nandan, 2011).Considering the scale and scope of company-relevant exchanges, it is pertinent that marketers monitor, join, and come to grips with efforts to decode "social media speak." Therefore the Following a brief introduction to this linguistic theory and its suitability for setting the research agenda, we explore a range of research directions that are both relevant and conceptually rob...

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An Improved Sentiment Classification using Lexicon into SVM

Cited by 5 publications

References 17 publications

Multistage Sentiment Classification Model using Malaysia Political Ontology

Multistage Sentiment Classification Model using Malaysia Political Ontology

A Recommendation Mechanism for Under-Emphasized Tourist Spots Using Topic Modeling and Sentiment Analysis

Decoding social media speak: developing a speech act theory research agenda

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