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Proceedings ArticleDOI

An artificial neural network based approach for sentiment analysis of opinionated text

23 Oct 2012-pp 37-42
TL;DR: A sentiment classification model using back-propagation artificial neural network (BPANN) is proposed that combines the strength of BPANN in classification accuracy with utilizing intrinsic domain knowledge available in the sentiment lexicons.
Abstract: The Internet and Web 2.0 social media have emerged as an important medium for expressing sentiments, opinions, evaluations, and reviews. Sentiment analysis or opinion mining is becoming an open research domain due to the abundance of discussion forums, Weblogs, e-commerce portals, social networking and content sharing sites where people tend to express their opinions. Sentiment Analysis involves classifying text documents based on the opinion expressed being positive or negative about a given topic. This paper proposes a sentiment classification model using back-propagation artificial neural network (BPANN). Information Gain and three popular sentiment lexicons are used to extract sentiment representing features that are then used to train and test the BPANN. This novel approach combines the strength of BPANN in classification accuracy with utilizing intrinsic domain knowledge available in the sentiment lexicons. The results obtained on the movie-review corpora have shown that the proposed approach has been able to reduce dimensionality, while producing accurate sentiment based classification of text.
Citations
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Book
01 Jan 2017
TL;DR: The SIIE 7th edition will be held in Marrakech, Morocco, in April 2017 as discussed by the authors, with the theme of " Trends in Technology Management and Economic Intelligence" (SIIE-7).
Abstract: The conference “SIIE” aims to develop the dialogue between experts and researchers from public and private sectors, to acquire basic and experimental on Information Systems (IS) and Economic Intelligence (EI) (or Competitive Intelligence in English acceptance and terminology). This promotes, in a risk environment, technologies related to economic intelligence. The dynamic of EI depends on the control of knowledge and requires competences to design the best strategies and ensure that decision-­makers take the right decisions. The international conference SIIE will be held in its seventh (7th.) edition in Marrakech in April 2017, after the six successful editions. This edition is organized by CIEMS and IEEE Technology & Engineering Management Society (TEMS), and sponsored by the Universities of Maghreb and Europe countries. The theme of SIIE is « Trends in Technology Management and Economic Intelligence ». Since 2008, the six proceedings and editions have allowed academic researchers and economic actors to achieve completed projects. The goal of SIIE is to continue in this way by creating opportunities, ideas and innovative ways to enhance projects, and build connections between universities and industries on both sides of the Mediterranean Sea. SIIE'2017 includes keynotes, tutorials, authors’ sessions and industrial panels, animated by experts, to identify new approaches and knowledge in economic intelligence, applied research and feedback. This will allow the emergence of new clusters in competitive intelligence. Within a convivial and comfortable framework, as Morocco knows so well how to offer such a framework, the SIIE conference has always been thought to promote the weaving of trust networks between actors in academia, industry and politics, thus contributing to the training of the SIIE scientific community. The expert recommendations and advices will help the SIIE community to find solutions to their many questions and problems.

12 citations

Book ChapterDOI
01 Jan 2018
TL;DR: A new model towards opinion mining and sentiment analysis of the text reviews posted in social media sites which are mostly in unstructured format is discussed, to automate the process of mining attitudes, opinions and hidden emotions from text.
Abstract: This paper discusses a new model towards opinion mining and sentiment analysis of the text reviews posted in social media sites which are mostly in unstructured format. In recent years, web forums and social media has become an excellent platform to express or share opinions in the form of text about any product or any interested topic. These opinions are used for making decisions to choose a product or any entity. Opinion mining and sentiment analysis are related in a sense that opining mining deals with analyzing and summarizing expressed opinions whereas sentiment analysis classifies opinionated text into positive and negative. Feature extraction is a crucial problem in sentiment analysis. Model proposed in the paper utilizes machine learning techniques and fuzzy approach for opinion mining and classification of sentiment on textual reviews. The goal is to automate the process of mining attitudes, opinions and hidden emotions from text.

11 citations

Book ChapterDOI
14 Jul 2018
TL;DR: The taxonomy of aspect based sentiment analysis is described with detailed explainaton of recent methods used and the pros and cons of research papers discussed are compared.
Abstract: Sentiment Analysis is the study of sentiments expressed by people. Aspect based Sentiment Analysis is the study of sentiments expressed by people regarding the aspect of an entity. Aspect based Sentiment Analysis is becoming an important task in realising the finer sentiments of objects as described by people in their opinions. In the present paper we describe several techniques which have come up in recent years involving aspect term extraction and/or aspect sentiment prediction.Present paper describes the taxonomy of aspect based sentiment analysis with detailed explainaton of recent methods used. This paper also gives the pros and cons of research papers discussed. In the present paper we have compared all the papers with table enteries.

9 citations

Journal ArticleDOI
TL;DR: An automatic lexicon generation in Bahasa Indonesia for sentiment analysis purpose shows promising results where it can predict the candidate’s rank, the election winner, and the percentage of votes for each candidate with better accuracy than the previous work which used manually generated lexicon.
Abstract: Sentiment analysis has been popularly used in analyzing data from the internet. One of the techniques used is lexicon based sentiment analysis. Generating lexicon is not an easy process, and lexicon in Bahasa Indonesia is rarely available. This paper proposes an automatic lexicon generation in Bahasa Indonesia for sentiment analysis purpose. Experiments were performed using the generated lexicon for doing sentiment analysis on Indonesian political news about the 2018 governor election in three provinces in Indonesia. The conducted experiments show promising results where it can predict the candidate’s rank, the election winner, and the percentage of votes for each candidate with better accuracy than the previous work which used manually generated lexicon.

5 citations


Cites methods from "An artificial neural network based ..."

  • ...Sharma and Dey showed that BPANN was performing well in sentiment analysis and reducing the dimensionali-ty....

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  • ...Sharma and Dey [15] used Back Propagation Artificial Neural Network (BPANN) to evaluate the sentiment of movie reviews....

    [...]

  • ...Besides performing sentiment analysis for the movie review, Sharma and Dey also analyzed the performance of BPANN....

    [...]

Proceedings ArticleDOI
01 Oct 2016
TL;DR: This paper presents a Web application prototype where from a review are returned the feeling, its features and other analysis metrics using Natural Language Processing and Sentiment Analysis in order to define important comments to be taken into consideration in the decision-making process.
Abstract: The evolution of e-commerce and Online Social Networks made significant g rowth of t he W eb a nd as consequence, available information increase quite every day, making the task of analyzing the reviews manually almost impossible for the decision-making process. Due to the amount of information, the creation of automatic methods of knowledge extraction and data mining has become necessary. This paper presents a Web application prototype where from a review are returned the feeling (positive, negative or neutral), its features and other analysis metrics using Natural Language Processing and Sentiment Analysis in order to define t he m ost important comments to be taken into consideration in the decision-making process. Experiments show efficacy i n t he p recision o f reviews with negative polarity and recall of reviews with positive polarity in 84.93% and 94.33% respectively and the most important comments were found in a measure considered satisfactory of 50% in F-Measure in both positive and neutral polarities.

5 citations


Cites background from "An artificial neural network based ..."

  • ...Por fim, a Seção 6 conclui o artigo e discute os trabalhos futuros....

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References
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Proceedings ArticleDOI
22 Aug 2004
TL;DR: This research aims to mine and to summarize all the customer reviews of a product, and proposes several novel techniques to perform these tasks.
Abstract: Merchants selling products on the Web often ask their customers to review the products that they have purchased and the associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. It also makes it difficult for the manufacturer of the product to keep track and to manage customer opinions. For the manufacturer, there are additional difficulties because many merchant sites may sell the same product and the manufacturer normally produces many kinds of products. In this research, we aim to mine and to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we only mine the features of the product on which the customers have expressed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a subset or rewrite some of the original sentences from the reviews to capture the main points as in the classic text summarization. Our task is performed in three steps: (1) mining product features that have been commented on by customers; (2) identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative; (3) summarizing the results. This paper proposes several novel techniques to perform these tasks. Our experimental results using reviews of a number of products sold online demonstrate the effectiveness of the techniques.

7,330 citations


"An artificial neural network based ..." refers background in this paper

  • ...Positive and negative sentiment based summaries for product features from reviews were proposed by Hu and Liu (2004)....

    [...]

01 Jan 2002
TL;DR: In this paper, the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, was considered and three machine learning methods (Naive Bayes, maximum entropy classiflcation, and support vector machines) were employed.
Abstract: We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we flnd that standard machine learning techniques deflnitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classiflcation, and support vector machines) do not perform as well on sentiment classiflcation as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classiflcation problem more challenging.

6,980 citations

Proceedings ArticleDOI
06 Jul 2002
TL;DR: This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging.
Abstract: We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classification problem more challenging.

6,626 citations

Posted Content
TL;DR: A simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (Thumbs down) if the average semantic orientation of its phrases is positive.
Abstract: This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., "subtle nuances") and a negative semantic orientation when it has bad associations (e.g., "very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word "excellent" minus the mutual information between the given phrase and the word "poor". A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.

4,526 citations

Journal ArticleDOI
TL;DR: The authors examine the effect of consumer reviews on relative sales of books at Amazon.com and Barnesandnoble.com, and find that reviews are overwhelmingly positive at both sites, but there are more reviews and longer reviews at Amazon and that an improvement in a book's reviews leads to an increase in relative sales.
Abstract: The authors examine the effect of consumer reviews on relative sales of books at Amazon.com and Barnesandnoble.com. The authors find that (1) reviews are overwhelmingly positive at both sites, but there are more reviews and longer reviews at Amazon.com; (2) an improvement in a book's reviews leads to an increase in relative sales at that site; (3) for most samples in the study, the impact of one-star reviews is greater than the impact of five-star reviews; and (4) evidence from review-length data suggests that customers read review text rather than relying only on summary statistics.

4,180 citations