Abstract
With the vast development of Internet technology 2.0, millions of people share their opinions on different social networking sites. To obtain the necessary information from the large volume of user-generated data, the attention on sentiment analysis among the research community is growing. The growth and prominence of sentiment analysis are 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 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 usergenerated content such as noisy, irrelevant information and fake reviews, there is a significant demand for a practical methodology that emphasizes the need for sentiment analysis. This study presents an exhaustive survey of the existing methodologies. It highlights the challenges and performance factors of various sentiment analysis approaches, including text preprocessing, opinion spam detection, and aspect level sentiment analysis.
Users use social media as a medium for their activities and are passionate about their posts on social networking platforms on various issues, topics, and events. Sentiment analysis plays a significant role in online e-commerce servicing sites in which users share their views and rating on products and services. With the help of sentiment analysis, companies identify customer dissatisfaction and enhance the quality of the products and services.
This study seeks diverse methods and performance measures on various application domains in sentiment analysis.
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 preprocessing, opinion spam detection, and aspect level sentiment analysis based on machine learning and deep learning.
User-generated content is growing worldwide, and people more eagerly express their views on social media towards various aspects. The opinionated text is challenging 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 user-generated data. 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.
Graphical Abstract
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