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A Study on Diverse Methods and Performance Measures in Sentiment Analysis

Author(s):

Meesala Shobha Rani and Sumathy Subramanian*   Pages 1 - 30 ( 30 )

Abstract:


With the vast development of internet technology 2.0, millions of people are sharing their opinions on different social networking sites. To obtain the necessary information from the huge volume of user-generated data, the attention on sentiment analysis among the research community is growing. Growth and prominence of sentiment analysis is synchronized with an increase in social media and networking sites. Users generally use natural language for speaking, writing, and expressing their views based on various sentiment orientations, ratings, and the features of different products, topics, and issues. This helps to produce ambiguity at the end of the customer's decision based on criticism to form an opinion based on such comments. To overcome the challenges of user-generated content such as noisy, irrelevant information and fake reviews, there is a significant demand for an effective methodology that emphasizes the need for sentiment analysis. This study presents an exhaustive survey of the existing methodologies and highlights the challenges and performance factors of various approaches of sentiment analysis including text preprocessing, opinion spam detection, and aspect level sentiment analysis.

Background: User-generated content is growing all over the globe and people more eagerly express their views on social media towards various aspects. The opinionated text is difficult to interpret and arrive at a conclusion based on the feedback gathered from reviews on various sites. Hence, the significance of sentiment analysis is growing to analyze the usergenerated data.

Objective: The paper presents an exhaustive review that provides an overview of the pros and cons of the existing techniques and highlights the current techniques in sentiment analysis namely text pre-processing, opinion spam detection, and aspect level sentiment analysis based on machine learning and deep learning. This will be useful to researchers who focus on the challenges very specifically and identify the most common challenges to work forward for a new solution.

Keywords:

Sentiment analysis, machine learning, lexicon based, hybrid based, opinion spam detection, text pre-processing, aspect based sentiment analysis

Affiliation:

School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore



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