A non-parametric LDA-based induction method for sentiment analysis

Shams, Mohammadreza; Shakery, Azadeh; Faili, Heshaam

doi:10.1109/aisp.2012.6313747

Cited by 30 publications

(14 citation statements)

References 13 publications

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“…Consequently, the major contribution of this study is to lay a foundation for a transition of prevailing technical viewpoints in the integration of LDA and SA to a user viewpoint. Indeed, there are various studies addressed the integration of LDA and SA through an improved semantic algorithm for LDA [38][39][40]. However, existing studies lack important discussion for how users perceive information delivered from LDA and SA.…”

Section: Discussionmentioning

confidence: 99%

Comparative Study on Perceived Trust of Topic Modeling Based on Affective Level of Educational Text

Park

Kim

et al. 2019

Applied Sciences

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Latent dirichlet allocation (LDA) is a representative topic model to extract keywords related to latent topics embedded in a document set. Despite its effectiveness in finding underlying topics in documents, the traditional algorithm of LDA does not have a process to reflect sentimental meanings in text for topic extraction. Focusing on this issue, this study aims to investigate the usability of both LDA and sentiment analysis (SA) algorithms based on the affective level of text. This study defines the affective level of a given set of paragraphs and attempts to analyze the perceived trust of the methodologies in regards to usability. In our experiments, the text of the college scholastic ability test was selected as the set of evaluation paragraphs, and the affective level of the paragraphs was manipulated into three levels (low, medium, and high) as an independent variable. The LDA algorithm was used to extract the keywords of the paragraph, while SA was used to identify the positive or negative mood of the extracted subject word. In addition, the perceived trust score of the algorithm was evaluated by the subjects, and this study verifies whether there is a difference in the score according to the affective levels of the paragraphs. The results show that paragraphs with low affect lead to the high perceived trust of LDA from the participants. However, the perceived trust of SA does not show a statistically significant difference between the affect levels. The findings from this study indicate that LDA is more effective to find topics in text that mainly contains objective information.

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Section: Discussionmentioning

confidence: 99%

Comparative Study on Perceived Trust of Topic Modeling Based on Affective Level of Educational Text

Park

Kim

et al. 2019

Applied Sciences

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“…Here is the review of various methods existing. Overview of sentiment analysis and its applications are provided in [1] Aspect based sentiment analysis with SenticLDA [31], LDA based non-parametric model [32], AS-LDA [33], multiaspect sentiment analysis [34], topic based mixture modeling [35] are other important contributions. A hybrid model based on LDA [36], probabilistic topic modeling [37], Concept Level Sentiment Analysis (CLSA) [38], probabilistic model based on syntax and topic for aspect based approach [39] and topic trends and user interests based topic model [40] are other useful approaches found for sentiment analysis.…”

Section: Related Workmentioning

confidence: 99%

A ML and NLP based Framework for Sentiment Analysis on Bigdata

Madhuri*¹,

Prasad²

2020

IJITEE

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Big data as multiple sources and social media is one of them. Such data is rich in opinion of people and needs automated approach with Natural Language Processing (NLP) and Machine Learning (ML) to obtain and summarize social feedback. With ML as an integral part of Artificial Intelligence (AI), machines can demonstrate intelligence exhibited by humans. ML is widely used in different domains. With proliferation of Online Social Networks (OSNs), people of all walks of life exchange their views instantly. Thus they became platforms where opinions or people are available. In other words, social feedback on products and services are available. For instance, Twitter produces large volumes of such data which is of much use to enterprises to garner Business Intelligence (BI) useful to make expert decisions. In addition to the traditional feedback systems, the feedback (opinions) over social networks provide depth in the intelligence to revise strategies and policies. Sentiment analysis is the phenomenon which is employed to analyze opinions and classify them into positive, negative and neutral. Existing studies usually treated overall sentiment analysis and aspect-based sentiment analysis in isolation, and then introduce a variety of methods to analyse either overall sentiments or aspect-level sentiments, but not both. Usage of probabilistic topic model is a novel approach in sentiment analysis. In this paper, we proposed a framework for comprehensive analysis of overall and aspect-based sentiments. The framework is realized with aspect based topic modelling for sentiment analysis and ensemble learning algorithms. It also employs many ML algorithms with supervised learning approach. Benchmark datasets used in international SemEval conferences are used for empirical study. Experimental results revealed the efficiency of the proposed framework over the state of the art.

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“…Several studies have been conducted on sentiment analysis in the Persian language. (Alimardani & Aghaei, 2015;Basiri, Naghsh-Nilchi, & Ghassem-Aghaee, 2014;Saraee & Bagheri, 2013;Shams, Shakery, & Faili, 2012). Some of them have led to generating a lexicon that is either for the general domain or domains other than the stock market.…”

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confidence: 99%

Tehran stock exchange prediction using sentiment analysis of online textual opinions

Ghahfarrokhi

Shamsfard

2020

Intell Sys Acc Fin Mgmt

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Summary We investigate the impact of social media data in predicting the Tehran Stock Exchange variables for the first time. We consider the closing price and daily return of three different stocks for this investigation. We collected our social media data from http://Sahamyab.com/stocktwits for about 3 months. To extract information from online comments, we propose a hybrid sentiment analysis approach that combines lexicon‐based and learning‐based methods. Since lexicons that are available for the Persian language are not practical for sentiment analysis in the stock market domain, we built a particular sentiment lexicon for this domain. After designing and calculating daily sentiment indices using the sentiment of the comments, we examine their impact on the baseline models that only use historical market data and propose new predictor models using multi‐regression analysis. In addition to the sentiments, we also examine the comments volume and the users' reliabilities. We conclude that the predictability of various stocks in the Tehran Stock Exchange is different depending on their attributes. Moreover, we indicate that only comments volume could be useful for predicting the closing price, and both the volume and the sentiment of the comments could be useful for predicting the daily return. We demonstrate that users' trust coefficients have different behaviours toward the three stocks.

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A non-parametric LDA-based induction method for sentiment analysis

Cited by 30 publications

References 13 publications

Comparative Study on Perceived Trust of Topic Modeling Based on Affective Level of Educational Text

Comparative Study on Perceived Trust of Topic Modeling Based on Affective Level of Educational Text

A ML and NLP based Framework for Sentiment Analysis on Bigdata

Tehran stock exchange prediction using sentiment analysis of online textual opinions

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