Abstract
Automatic classification of product reviews into interrogative and noninterrogative: Generating real time answer
Highlights
Posted reviews on the relevant webpages about a product motivate the company to enhance quality and it helps users to decide in favor of purchasing the product
Question conveying and not conveying Information (Zhao and Mei, 2013), identifying Answer Seeking questions from Arabic tweets (Hasanain et al, 2014), extraction of subjective/objective and questions and Rhetorical Questions (Hasanain et al, 2014; Liu and Jansen, 2015; Ranganath et al, 2016; Liu and Jansen, 2016), investigation of questions asked by Arab journalists (Hasanain et al, 2016), and detection of user intent behind asking the question (Kharche and Mante, 2017) have been studied
We propose a generation of real time answer from interrogative based on extracted aspects with user friendly interface
Summary
Academia both sentiment analysis and opinion mining are frequently employed. As discussed above, pervasive real-life applications are only part of the reason why sentiment analysis is a popular research problem. Besides covering classifications of reviews from different dimensions, there is still need of classification of reviews with respect to interrogatives and non-interrogatives In this regard, question conveying and not conveying Information (Zhao and Mei, 2013), identifying Answer Seeking questions from Arabic tweets (Hasanain et al, 2014), extraction of subjective/objective and questions and Rhetorical Questions (Hasanain et al, 2014; Liu and Jansen, 2015; Ranganath et al, 2016; Liu and Jansen, 2016), investigation of questions asked by Arab journalists (Hasanain et al, 2016), and detection of user intent behind asking the question (Kharche and Mante, 2017) have been studied. We propose a generation of real time answers using Latent Semantic Indexing (LSI) scores (without aspect)
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