Understanding and Predicting Question Subjectivity in Social Question and Answering

Liu, Zhe; Jansen, Bernard J.

doi:10.1109/tcss.2016.2564400

Cited by 9 publications

(6 citation statements)

References 31 publications

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“…In 2016, Liu and Jansen [7] have designed a scheme based on intent detection as a binary classification issue, and therefore for every question, subjective and objective classes were portrayed. A wide-ranging set of lexical, contextual, and syntactical features were exploited to construct the classifier and the investigational outcomes demonstrate reasonable classification behavior.…”

Section: Literature Reviewmentioning

confidence: 99%

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Hybrid CNN-LSTM Model for Answer Identification

Moholkar¹,

Patil²

2019

IJRTE

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User quest for information has led to development of Question Answer (QA) system to provide relevant answers to user questions. The QA task are different than normal NLP tasks as they heavily depend to semantics and context of given data. Retrieving and predicting answers to verity of questions require understanding of question, relevance with context and identifying and retrieving of suitable answers. Deep learning helps to produce impressive performance as it employs deep neural network with automatic feature extraction methods. The paper proposes a hybrid model to identify suitable answer for posed question. The proposes power exploits the power of CNN for extracting features and ability of LSTM for considering long term dependencies and semantic of context and question. Paper provides a comparative analysis on deep learning methods useful for predicting answer with the proposed method .The model is implemented on twenty tasks of babI dataset of Facebook .

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Section: Literature Reviewmentioning

confidence: 99%

“…It intends at deriving constructive QA pairs [4] [5] from various CQA outfits. The major complexity relies in how to link the semantic gaps among QA [6] [7] pairs. Supervised machine learning is a typical one in dealing with certain problems namely, deep learning and statistical learning.…”

Section: Introductionmentioning

confidence: 99%

Hybrid CNN-LSTM Model for Answer Identification

Moholkar¹,

Patil²

2019

IJRTE

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“…With respect to the web social Q&A platform, users can quickly post long questions and offer detailed descriptions; meanwhile, other users can contribute comprehensive and informative answers through such a platform (Liu and Jansen, 2016;Shah et al, 2009). Additionally, users on the web social Q&A platform can share professional knowledge efficiently because web-based platforms have an advantage in processing large-scale content sharing (Sun et al, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Additionally, users on the web social Q&A platform can share professional knowledge efficiently because web-based platforms have an advantage in processing large-scale content sharing (Sun et al, 2016). Last but not the least, compared to the mobile version, web social Q&A platforms tend to update their services functions on time because websites can provide adequate spaces for social Q&A providers to develop such functions (Liu and Jansen, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Comparing the web and mobile platforms of a social Q&A service from the user’s perspective

Chen

Chua

Deng

2018

AJIM

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Purpose As an increasing number of users have acquired information across the web and mobile platforms for social question and answering (Q&A), it is of interest to explore whether there are differences in social Q&A usages between the two platforms. The purpose of this paper is to compare web and mobile platforms of a social Q&A service from the user’s perspective in terms of three dimensions, namely, demographics, individual-based constructs, and information-based constructs. Design/methodology/approach Because Zhihu.com is one of the most popular social Q&A sites in China, the authors used online questionnaires to investigate its users’ perceptions of these three dimensions. From January to March 2016, the authors obtained 278 valid responses in total through snowball and convenient sampling. Collected data are analyzed through descriptive statistics and inferential statistics. Findings The results indicate that there exist significant differences between web users and mobile users on Zhihu.com in terms of gender, affinity, and information seeking. More specifically, compared to the male users, more female users rely on the mobile platform to access the information service; mobile users perceive higher affinity with Zhihu.com than web users; and mobile users perceive higher information-seeking intention than web users do. Originality/value Regarding the theoretical aspect, this study proposes a conceptual framework for comparison between the web and mobile platforms of social Q&A from the user’s perspective. Regarding the practical aspect, the comparative results of this study could give social Q&A service providers useful information about users’ differences between web and mobile platforms of social Q&A services.

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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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Understanding and Predicting Question Subjectivity in Social Question and Answering

Cited by 9 publications

References 31 publications

Hybrid CNN-LSTM Model for Answer Identification

Hybrid CNN-LSTM Model for Answer Identification

Comparing the web and mobile platforms of a social Q&A service from the user’s perspective

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

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