Social Network Opinion Mining and Sentiment Analysis: Classification Approaches, Trends, Applications and Issues

Pimpalkar, Amit; Raj, R. Jeberson Retna

doi:10.1007/978-981-33-6981-8_60

Cited by 7 publications

(1 citation statement)

References 27 publications

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“…Opinion mining is a technique to locate and analyze personal information by using computational linguistics, text analysis, and natural language processing ( Zong, Xia & Zhang, 2021 ). Some popular opinion-mining techniques are lexicon-based approaches, Aspect-Based Sentiment Analysis, and rule-based approaches used in sentiment analysis, language translation and text summarization ( Pimpalkar & Jeberson Retna Raj, 2021 ). It is a subdomain of text mining utilized to recognize and retrieve the desired information from text by creating automated systems.…”

Section: Introductionmentioning

confidence: 99%

Contradiction in text review and apps rating: prediction using textual features and transfer learning

Aljrees,

Umer,

Saidani

et al. 2024

PeerJ Computer Science

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Mobile app stores, such as Google Play, have become famous platforms for practically all types of software and services for mobile phone users. Users may browse and download apps via app stores, which also help developers monitor their apps by allowing users to rate and review them. App reviews may contain the user’s experience, bug details, requests for additional features, or a textual rating of the app. These ratings can be frequently biased due to inadequate votes. However, there are significant discrepancies between the numerical ratings and the user reviews. This study uses a transfer learning approach to predict the numerical ratings of Google apps. It benefits from user-provided numeric ratings of apps as the training data and provides authentic ratings of mobile apps by analyzing users’ reviews. A transfer learning-based model ELMo is proposed for this purpose which is based on the word vector feature representation technique. The performance of the proposed model is compared with three other transfer learning and five machine learning models. The dataset is scrapped from the Google Play store which extracts the data from 14 different categories of apps. First, biased and unbiased user rating is segregated using TextBlob analysis to formulate the ground truth, and then classifiers prediction accuracy is evaluated. Results demonstrate that the ELMo classifier has a high potential to predict authentic numeric ratings with user actual reviews.

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Section: Introductionmentioning

confidence: 99%