Challenges and solutions in the opinion summarization of user-generated content

Balahur, Alexandra; Kabadjov, Mijail; Shinar, J.; Steinberger, Ralf; Montoyo, Andrés

doi:10.1007/s10844-011-0194-z

Cited by 22 publications

(22 citation statements)

References 13 publications

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“…The opinion summary is explored based on the opinion sentences. This paper summarizes only factual information, and is not useful for the opinion-based summary [1]. In order to avoid the complex space modelling of various sources a tensor based information framework was introduced to predict stock [2].…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis for Effective Stock Market Prediction

Bharathi¹,

Geetha²

2017

IJIES

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Abstract:The Stock market forecasters focus on developing a successful approach to predict stock prices. The vital idea to successful stock market prediction is not only achieving best results but also to minimize the inaccurate forecast of stock prices. This paper attempts to design and implement a predictive system for guiding stock market investment. The novelty of our approach is the combination of both sensex points and Really Simple Syndication (RSS) feeds for effective prediction. Our claim is that the sentiment analysis of RSS news feeds has an impact on stock market values. Hence RSS news feed data are collected along with the stock market investment data for a period of time. Using our algorithm for sentiment analysis, the correlation between the stock market values and sentiments in RSS news feeds are established. This trained model is used for prediction of stock market rates. In our experimental study the stock market prices and RSS news feeds are collected for the company ARBK from Amman Stock Exchange (ASE). Our experimental study has shown an improvement of 14.43% accuracy prediction, when compared with the standard algorithm of ID3, C4.5 and moving average stock level indicator.

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Section: Related Workmentioning

confidence: 99%

Sentiment Analysis for Effective Stock Market Prediction

Bharathi¹,

Geetha²

2017

IJIES

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“…Many researchers performed sentiment analysis on different domains data such as movie [4], product [13], and social network [3] [15]. Many researchers also worked on summarization of reviews [1] [6] [7]. In a survey of Pang and Lee [9] they presented concept of opinion mining, its application and challenges involved it.…”

Section: Related Workmentioning

confidence: 99%

“…Classification algorithms such as Naive Bayes [11], SVM [4], and Decision Tree [1] work well for review text. Other methods such as resource based opinion mining [6], [7] also proposed by different researchers. It depends upon resources such as SentiWordNet [10], WordNet Affect [14] which contain polarity values of word for sentiment classification.…”

Section: Related Workmentioning

confidence: 99%

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Opinion Mining and Summarization of Hotel Reviews

Raut

Londhe

2014

2014 International Conference on Computational Intelligence and Communication Networks

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Everyday many users purchases product, book travel tickets, buy goods and services through web. Users also share their views about product, hotel, news, topic etc on web in the form of reviews, blogs, comments etc. Many users read review information given on web to take decisions such as buying products, watching movie, going to restaurant etc. Reviews contain user's opinion about product, event or topic. It is difficult for web users to read and understand contents from large number of reviews. Important and useful information can be extracted from reviews through opinion mining and summarization process. We presented machine learning and SentiWordNet based method for opinion mining from hotel reviews and sentence relevance score based method for opinion summarization of hotel reviews. We obtained about 87% of accuracy of hotel review classification as positive or negative review by machine learning method. The classified and summarized hotel review information helps web users to understand review contents easily in a short time.

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“…A informação obtida tem grande valor para tomada de decisão em inteligência de negócios e de governo. Alguns exemplos de aplicação são: previsão de desempenho de vendas (Liu et al, 2007), análise de postagens no Twitter para entender o comportamento do mercado (Bollen et al, 2011), distribuição estratégica de propagandas em páginas web (Jin et al, 2007), elaboração de sistemas de recomendação (Jakob et al, 2009), sumarização automática (Balahur et al, 2012;Kabadjov et al, 2011), previsão de resultados de eleições na política (Tumasjan et al, 2010) e análise de posições políticas (Laver et al, 2003).…”

Section: Contextualização E Motivaçãounclassified

Sobre normalização e classificação de polaridade de textos opinativos na web

Avanço¹

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Challenges and solutions in the opinion summarization of user-generated content

Cited by 22 publications

References 13 publications

Sentiment Analysis for Effective Stock Market Prediction

Sentiment Analysis for Effective Stock Market Prediction

Opinion Mining and Summarization of Hotel Reviews

Sobre normalização e classificação de polaridade de textos opinativos na web

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