Root Cause Analysis Based on Relations Among Sentiment Words

Park, Sang Min; Kim, Young-Gab

doi:10.1007/s12559-021-09872-3

“…Sun [29] proposed a framework that learns the patterns of new spam activities that involves the detection of blacklisted URLs, near duplicates of confidentially labeled tweets, spam word detection and multi-classifier-based detectors. S. M. Park and Y. G. Kim [30] proposed a method that analyses the causal relationship between the sentiment words. They analyzed the root cause of negative opinion in sentiment analysis.…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis of twitter data to detect and predict political leniency using natural language processing

Kowsik,

Yashwanth,

Harish

et al. 2024

J Intell Inf Syst

1

0

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This paper analyses twitter data to detect the political lean of a profile by extracting and classifying sentiments expressed through tweets. The work utilizes natural language processing, augmented with sentiment analysis algorithms and machine learning techniques, to classify specific keywords of interest as either positive or negative, based on the sentiment score of each keyword. The proposed methodology initially performs data pre-processing, followed by multi-aspect sentiment analysis for computing the sentiment score of the extracted keywords, which are then used for precisely classifying users into various clusters based on similarity score with respect to a sample user in each cluster. The proposed technique also predicts the sentiment of a profile towards unknown keywords and gauges the bias of an unidentified user towards political events or social issues. The proposed technique was tested on Twitter dataset with 1.72 million tweets taken from over 10,000 profiles, and was able to successfully identify the political leniency of the user profiles with 99% confidence level. The paper could also identify the impact of political decisions on various clusters, by analyzing the shift in number of users belonging to the different clusters.

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An AI based cross‐language aspect‐level sentiment analysis model using English corpus

Chen,

Pan

2024

Engineering Reports

1

0

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Accurate cross‐language aspect‐level sentiment analysis methods can provide accurate decision support for social networks, e‐commerce platforms, and other platforms, thereby providing users with higher quality services. However, actual data is very complex and contains a large amount of redundant information. Existing methods face challenges in extracting semantic association information and deep emotional features when dealing with this complex data. To address these issues, an aspect‐level sentiment analysis model (called Multi‐XLNet‐RCNN) is proposed that integrates multi‐channel XLNet and RCNN. First, a multi‐channel XLNet (Multi XLNet) network model is used to perform autoregressive encoding operations on different languages, fully extracting contextual information from the text and better characterizing the ambiguity of the text. Then, in the RCNN module, the contextual features output by the BiGRU layer are concatenated with the pre trained input features to extract deeper emotional features. Finally, in response to the issue of inconsistent aspect‐level information in sentence features extracted from different language channels, a multi head attention mechanism based on aspect class interaction is utilized to obtain a text attention emotion representation for a given aspect, thereby improving the accuracy of aspect‐level emotion classification. The experiment uses the public English corpus provided by SemEval 2016 as the source language, and Chinese comment data on Dianping and JD E‐commerce platforms as the target language. The experimental results show that the proposed Multi XLNet‐RCNN sentiment analysis method can achieve accurate aspect‐level Sentiment analysis, and the accuracy rates on the two data sets of Dianping and Jingdong E‐commerce can be as high as 0.851 and 0.792, respectively, superior to other advanced comparison models. This model has good application value in cross‐language analysis of social networks and e‐commerce platforms.

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Sentiment Analysis of Twitter Data to Detect and Predict Political Leniency Using Natural Language Processing

Kowsik,

Yashwanth,

Harish

et al. 2023

Preprint

0

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This paper analyses twitter data to detect the political lean of a profile by extracting and classifying sentiments expressed through tweets. The work utilizes natural language processing, augmented with sentiment analysis algorithms and machine learning techniques, to classify specific keywords of interest as either positive or negative, based on the sentiment score of each keyword. The proposed methodology initially performs data pre-processing, followed by multi-aspect sentiment analysis for computing the sentiment score of the extracted keywords, which are then used for precisely classifying users into various clusters based on similarity score with respect to a sample user in each cluster. The proposed technique also predicts the sentiment of a profile towards unknown keywords and gauges the bias of an unidentified user towards political events or social issues. The proposed technique was tested on Twitter dataset with 1.72 million tweets taken from over 10,000 profiles, and was able to successfully identify the political leniency of the user profiles with 99% confidence level. The paper could also identify the impact of political decisions on various clusters, by analyzing the shift in number of users belonging to the different clusters.

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Root Cause Analysis Based on Relations Among Sentiment Words

Cited by 3 publications

References 43 publications

Sentiment analysis of twitter data to detect and predict political leniency using natural language processing

Sentiment analysis of twitter data to detect and predict political leniency using natural language processing

An AI based cross‐language aspect‐level sentiment analysis model using English corpus

Sentiment Analysis of Twitter Data to Detect and Predict Political Leniency Using Natural Language Processing

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