An Efficient Way of Answering the Questions Asked on Social Sites by Understanding User Intent

Kharche, Shital E.; Mante, R. V.

doi:10.1109/icrteect.2017.22

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…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. However, they all used the data readily available in the form of interrogatives.…”

Section: Introductionmentioning

confidence: 99%

Automatic classification of product reviews into interrogative and noninterrogative: Generating real time answer

al.¹

2019

Int. j. adv. appl. sci.

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Posted reviews on the relevant webpages about a product not only motivate the company to enhance quality but also it helps users to decide in favor of (or against) purchasing the product. These reviews are classified by different researchers through subjectivity based, entity based, or aspect based to find the polarity using the supervised or unsupervised technique. However, classification based on interrogatives and non-interrogatives is not handled yet. Datasets of interrogatives are analyzed as identifying Answer Seeking questions from Arabic tweets, question conveying and not conveying Information, Rhetorical Questions while here classifying the sentences into interrogatives and non-interrogatives is the preliminary step, which is a core contribution of proposed work. If detected questions are answered and moreover real time, it could not only motivate a user positively to buy the product but also users feel full duplex communication. In this work, we formulated this problem proposing linguistic and heuristic rules that automatically senses the interrogative and answer promptly based on the aforementioned aspect. If there is no aspect in an asked question, then LSI (Latent Semantic Indexing) generate answer using classified noninterrogatives. LSI is an efficient information retrieval algorithm, which finds the closest document to a given query. Experimental results using two publically available datasets show a precision of 95% and 96% which has 10% increased performance than alternatives machine learning methods Meta Filtered Classifier and Naive Bayes.

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Section: Introductionmentioning

confidence: 99%

Automatic classification of product reviews into interrogative and noninterrogative: Generating real time answer

al.¹

2019

Int. j. adv. appl. sci.

View full text Add to dashboard Cite

show abstract

An Efficient Way of Answering the Questions Asked on Social Sites by Understanding User Intent

Cited by 1 publication

References 19 publications

Automatic classification of product reviews into interrogative and noninterrogative: Generating real time answer

Automatic classification of product reviews into interrogative and noninterrogative: Generating real time answer

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