Sentence Level Sentiment Identification and Calculation from News Articles Using Machine Learning Techniques

Shirsat, Vishal S.; Jagdale, Rajkumar S.; Deshmukh, Sachin N.

doi:10.1007/978-981-13-1513-8_39

Cited by 29 publications

(15 citation statements)

References 7 publications

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“…Figure 1: Proposed approach for answer detection in discussion forums. 4 Scientific Programming (b) Some features are correlated while some are obtained from other feature combination (c) Not all features are available in datasets (d) Using forum-specific features makes the model forum dependent (e) Using all of them is computationally expensive To overcome the above limitations, initially we select those features whose availability is hundred percent and can be easily calculated from the text as discussed in Section 3.2.…”

Section: Answer Classification Resultsmentioning

confidence: 99%

“…ere are two types of features, lexical and nonlexical. Both of them are used to find reply relevancy and similarity with the given question [1][2][3][4][5]. Some nonlexical features are not always available [6], which cannot be calculated easily and also make the model forum dependent, e.g., if forum metadata are used for training the model, then the model becomes dependent on those specific features, and hence it cannot be easily adapted to other forums.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Approach for Answer Detection in Discussion Forums: An Application of Big Data Analytics

Khan

Ibrahim

Uddin

et al. 2020

Scientific Programming

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Nowadays, data are flooding into online web forums, and it is highly desirable to turn gigantic amount of data into actionable knowledge. Online web forums have become an integral part of the web and are main sources of knowledge. People use this platform to post their questions and get answers from other forum members. Usually, an initial post (question) gets more than one reply posts (answers) that make it difficult for a user to scan all of them for most relevant and quality answer. Thus, how to automatically extract the most relevant answer for a question within a thread is an important issue. In this research, we treat the task of answer extraction as classification problem. A reply post can be classified as relevant, partially relevant, or irrelevant to the initial post. To find the relevancy/similarity of a reply to the question, both lexical and nonlexical features are used. We proposed to use LinearSVC, a variant of support vector machine (SVM), for answer classification. Two selection techniques such as chi-square and univariate are employed to reduce the feature space size. The experimental results showed that LinearSVC classifier outperformed the other state-of-the-art classifiers in the context of classification accuracy for both Ubuntu and TripAdvisor (NYC) discussion forum datasets.

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Section: Answer Classification Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach for Answer Detection in Discussion Forums: An Application of Big Data Analytics

Khan

Ibrahim

Uddin

et al. 2020

Scientific Programming

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“…We categorize the above features into two types, lexical and nonlexical, as shown in Table 1. Lexical features are further classified into syntactic, string-based, and semantic features that are used for general text classification [7] and specifically for answer relevancy/similarity with the given question in discussion forums [8][9][10][11][12]. e authors in [8] used both lexical and nonlexical types to classify reply post as non-quality, low quality, and high quality.…”

Section: Content-basedmentioning

confidence: 99%

“…is group of supervised learning algorithms is used for regression, outlier detection, and classification-related task. It uses less memory and performs efficiently in high-dimensional space [11]. It uses different kernels, but a custom kernel can also be specified.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Impact of Lexical Features on Answer Detection Model in Discussion Forums

et al. 2021

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Online forums have become the main source of knowledge over the Internet as data are constantly flooded into them. In most cases, a question in a web forum receives several responses, making it impossible for the question poster to obtain the most suitable answer. Thus, an important problem is how to automatically extract the most appropriate and high-quality answers in a thread. Prior studies have used different combinations of both lexical and nonlexical features to retrieve the most relevant answers from discussion forums, and hence, there is no standard/general set of features that could be effectively used for relevant answer/reply post classification. However, this study proposed an answer detection model that is exclusively relying on lexical features and employs a random forest classifier for classification of answers in discussion boards. Experimental results showed that the proposed answer detection model outperformed the baseline technique and other state-of-the-art machine learning algorithms in terms of classification accuracy on benchmark forum datasets.

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“…(Shirsat, Vishal S et al, 2019) [13] discussed about sentence level sentiment identification by performing research on news articles. The data (of news articles) was extracted from BBC news and sentence-level negation identification was basically applied.…”

Section: Literature Reviewmentioning

confidence: 99%

Establishing News Credibility using Sentiment Analysis on Twitter

Sharf¹,

Jalil²,

Amir³

et al. 2019

IJACSA

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The widespread use of Internet has resulted in a massive number of websites, blogs and forums. People can easily discuss with each other about different topics and products, and can leave reviews to help out others. This automatically leads to a necessity of having a system that may automatically extract opinions from those comments or reviews to perform a strong analysis. So, it may help out businesses to know the opinions of people about their products/services so they can make further improvements. Sentiment Analysis or Opinion Mining is the system that intelligently performs classification of sentiments by extracting those opinions or sentiments from the given text (or comments or reviews). This paper presents a thorough research work carried out on tweets' sentiment analysis. An area-specific analysis is done to determine the polarity of extracted tweets for make an automatic classification that what recent news people have liked or disliked. The research is further extended to perform retweet analysis to describe the redistribution of reactions on a specific twitter post (or tweet).

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Sentence Level Sentiment Identification and Calculation from News Articles Using Machine Learning Techniques

Cited by 29 publications

References 7 publications

Machine Learning Approach for Answer Detection in Discussion Forums: An Application of Big Data Analytics

Machine Learning Approach for Answer Detection in Discussion Forums: An Application of Big Data Analytics

Impact of Lexical Features on Answer Detection Model in Discussion Forums

Establishing News Credibility using Sentiment Analysis on Twitter

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