Abstract
Appropriate identification and classification of online reviews to satisfy the needs of current and potential users pose a critical challenge for the business environment. This paper focuses on a specific kind of reviews: the suggestive type. Suggestions have a significant influence on both consumers' choices and designers' understanding and, hence, they are key for tasks such as brand positioning and social media marketing. The proposed approach consists of three main steps: (1) classify comparative and suggestive sentences; (2) categorize suggestive sentences into different types, either explicit or implicit locutions; (3) perform sentiment analysis on the classified reviews. A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. Experimental results for all three tasks are obtained on a dataset of mobile phone reviews and demonstrate that extending a bag-of-words representation with suggestive and comparative patterns is ideal for distinguishing suggestive sentences. In particular, it is observed that classifying suggestive sentences into implicit and explicit locutions works best when using a mixed sequential rule feature representation. Sentiment analysis achieves maximum performance when employing additional preprocessing in the form of negation handling and target masking, combined with sentiment lexicons.
Highlights
The emergence of Web 2.0 technologies and the growing number of online reviews websites, such as Amazon, Epinions, and Cnet, emphasize user participation
The contributions made through this study are as follows: (1) propose a study of the problem with classifying suggestive sentences in text; (2) categorize suggestive sentences into different types based on linguistic research; (3) perform sentiment analysis on classified suggestive reviews
The above linguistic classification of suggestive sentences has two limitations: (1) nonsuggestive with suggestive words; (2) limited coverage or suggestives with nonsuggestive words
Summary
The emergence of Web 2.0 technologies and the growing number of online reviews websites, such as Amazon, Epinions, and Cnet, emphasize user participation. Sentiment analysis ( commonly referred to as opinion mining) is a natural language processing task that aims to track the public’s mood regarding a particular product or service. This type of text analysis belongs to the field of natural language processing, computational linguistics, and text mining [1] It is cumbersome and there is high time overhead for a human reader to find appropriate resources, extract opinion sentences, read, and summarize them to obtain useful information. The contributions made through this study are as follows: (1) propose a study of the problem with classifying suggestive sentences in text (to the best of our knowledge, there is no reported study on this matter so far); (2) categorize suggestive sentences into different types based on linguistic research; (3) perform sentiment analysis on classified suggestive reviews. This paper is organized as follows: in Section 2, related work is presented; Section 3 proposes the problem statement and categorizes different types of suggestive sentences, expanding on what is already available in linguistics; Section 4 presents the proposed technique; Section 5 concludes the paper and suggests future directions
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