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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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Journal ArticleDOI
TL;DR: An encoding-based Wave2vec time series classifier model, which combines signal-processing and deep learning-based natural language processing techniques, is provided, which facilitates intuitive and easy recognition, and identification of influential patterns.
Abstract: Interest in research involving health-medical information analysis based on artificial intelligence, especially for deep learning techniques, has recently been increasing. Most of the research in this field has been focused on searching for new knowledge for predicting and diagnosing disease by revealing the relation between disease and various information features of data. These features are extracted by analyzing various clinical pathology data, such as EHR (electronic health records), and academic literature using the techniques of data analysis, natural language processing, etc. However, still needed are more research and interest in applying the latest advanced artificial intelligence-based data analysis technique to bio-signal data, which are continuous physiological records, such as EEG (electroencephalography) and ECG (electrocardiogram). Unlike the other types of data, applying deep learning to bio-signal data, which is in the form of time series of real numbers, has many issues that need to be resolved in preprocessing, learning, and analysis. Such issues include leaving feature selection, learning parts that are black boxes, difficulties in recognizing and identifying effective features, high computational complexities, etc. In this paper, to solve these issues, we provide an encoding-based Wave2vec time series classifier model, which combines signal-processing and deep learning-based natural language processing techniques. To demonstrate its advantages, we provide the results of three experiments conducted with EEG data of the University of California Irvine, which are a real-world benchmark bio-signal dataset. After converting the bio-signals (in the form of waves), which are a real number time series, into a sequence of symbols or a sequence of wavelet patterns that are converted into symbols, through encoding, the proposed model vectorizes the symbols by learning the sequence using deep learning-based natural language processing. The models of each class can be constructed through learning from the vectorized wavelet patterns and training data. The implemented models can be used for prediction and diagnosis of diseases by classifying the new data. The proposed method enhanced data readability and intuition of feature selection and learning processes by converting the time series of real number data into sequences of symbols. In addition, it facilitates intuitive and easy recognition, and identification of influential patterns. Furthermore, real-time large-capacity data analysis is facilitated, which is essential in the development of real-time analysis diagnosis systems, by drastically reducing the complexity of calculation without deterioration of analysis performance by data simplification through the encoding process.

22 citations


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

  • ...Document classification, another instance of the sequence classification problem, is performed by using the order of words as a major feature [25], a sentimental analysis is performed by using the sentiment lexicons of words [26], and the pattern-based sequence classification is studied in more general areas [27]....

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01 Jan 2016
TL;DR: The system performs well for recurrent neural network when compared with the system submitted to the shared task as the accuracy of the system had increased and the network seeks to pursue sentiment oriented feature which improves in analyzing the sentiments on tweets.
Abstract: This paper aims at improving the system which is submitted to the shared task on Sentiment Analysis in Indian Languages (SAIL2015) at MIKE 2015. In this work the tweets are classified into three polarity category namely positive, negative and neutral. Twitter data of three languages namely Tamil, Hindi and Bengali are already provided by SAIL 2015 task organizers as we have participated in the contest. Recurrent neural network is used for analyzing the sentiment in the tweets. The system performs well for recurrent neural network when compared with the system submitted to the shared task as the accuracy of the system had increased. This is due to the fact that the recurrent neural network concentrates more on language specific feature. In training, the recurrent neural network tries to learn based on the error that are generated as intermediate output. By this way the network seeks to pursue sentiment oriented feature which improves in analyzing the sentiments on tweets. We have obtained a state accuracy for the proposed system, where we achieved an accuracy of 88%, 72.01% and 65.16% for Tamil, Hindi and Bengali languages respectively for SAIL 2015 dataset.

19 citations


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

  • ...In this work along with BPANN, it uses domain knowledge which are available in sentiment lexicon [8]....

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Journal ArticleDOI
10 Mar 2020-PLOS ONE
TL;DR: The results suggest that institutional and departmental barriers driven by power asymmetries play a large role in the underreporting of sexual harassment among students—especially those in STEM disciplines.
Abstract: What factors predict the underreporting of sexual harassment in academe? We used logistic regression and sentiment analysis to examine 2,343 reports of sexual harassment involving members of university communities. Results indicate students were 1.6 times likely to not report their experiences when compared to faculty. Respondents in the life and physical sciences were 1.7 times more likely to not report their experiences when compared to respondents in other disciplines. Men represented 90% of the reported perpetrators of sexual harassment. Analysis of respondents' written accounts show variation of overall sentiment based on discipline, student type, and the type of institution attended, particularly with regard to mental health. Our results suggest that institutional and departmental barriers driven by power asymmetries play a large role in the underreporting sexual harassment among students-especially those in STEM disciplines.

19 citations

Proceedings ArticleDOI
01 Jan 2018
TL;DR: A new topic model based approach for opinion mining and sentiment analysis of text reviews posted in web forums or social media site which are mostly in unstructured in nature is discussed.
Abstract: This paper discusses a new topic model based approach for opinion mining and sentiment analysis of text reviews posted in web forums or social media site which are mostly in unstructured in nature. In recent years, opinions are exchanged in clouds about any product, person, event or any interested topic. These opinions help in decision making for choosing a product or getting feedback about any topic. 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. Aspect extraction is a crucial problem in sentiment analysis. Model proposed in the paper utilizes topic model for aspect extraction and support vector machine learning technique for sentiment classification of textual reviews. The goal is to automate the process of mining attitudes, opinions and hidden emotions from text.

12 citations


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

  • ...On reviewing literature, it is found that various supervised machine learning algorithms like Naïve Bayes [12] [13] Support Vector Machines[14] [15] and Neural Networks [16] have been used for opinion mining of text to classify positive and negative sentiment....

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Proceedings ArticleDOI
01 Jul 2016
TL;DR: This work proposes an experimental study between their approach using Fuzzy Systems and an execution using Artificial Neural Network to verify which is the most appropriate to solve the problem to estimate the importance of reviews.
Abstract: With the evolution of e-commerce and Online Social Networks, the web information has constantly increased, so the relevance to create methods for automatic knowledge extraction and data mining earned notoriety. Information as opinion evaluation is a point studied by Sentiment Analysis area, which is becoming important nowadays. Be aware of the best reviews is a factor that must be taken into account. Sousa et al. proposed an approach to estimate the degree of importance of reviews about product and services using Fuzzy System, reporting good results. This work proposes an experimental study between their approach using Fuzzy Systems and an execution using Artificial Neural Network to verify which is the most appropriate to solve the problem to estimate the importance of reviews.

12 citations


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

  • ...Sharma and Dey [10] proposed a sentiment classification model using backpropagation artificial neural network, using information gain and three popular sentiment lexicons to extract sentiment representing features that are then used to train and test the network....

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References
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Proceedings Article
01 Jan 2007
TL;DR: A system that assigns scores indicating positive or negative opinion to each distinct entity in the text corpus, consisting of a sentiment identication phase, and a sentiment aggregation and scoring phase, which scores each entity relative to others in the same class.
Abstract: Newspapers and blogs express opinion of news entities (people, places, things) while reporting on recent events. We present a system that assigns scores indicating positive or negative opinion to each distinct entity in the text corpus. Our system consists of a sentiment identication phase, which associates expressed opinions with each relevant entity, and a sentiment aggregation and scoring phase, which scores each entity relative to others in the same class. Finally, we evaluate the signicance of our scoring techniques over large corpus of news and blogs.

555 citations


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

  • ...In an attempt to assigns sentiment scores to each distinct entity in the text and then assigning a overall subjectivity score to the text, Godbole et al. (2007) proposed a sentiment lexiconbased semantic approach [12]....

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Journal ArticleDOI
TL;DR: The experimental results indicate that IG performs the best for sentimental terms selection and SVM exhibits the best performance for sentiment classification, and it is found that sentiment classifiers are severely dependent on domains or topics.
Abstract: Up to now, there are very few researches conducted on sentiment classification for Chinese documents. In order to remedy this deficiency, this paper presents an empirical study of sentiment categorization on Chinese documents. Four feature selection methods (MI, IG, CHI and DF) and five learning methods (centroid classifier, K-nearest neighbor, winnow classifier, Naive Bayes and SVM) are investigated on a Chinese sentiment corpus with a size of 1021 documents. The experimental results indicate that IG performs the best for sentimental terms selection and SVM exhibits the best performance for sentiment classification. Furthermore, we found that sentiment classifiers are severely dependent on domains or topics.

430 citations

Proceedings ArticleDOI
31 Oct 2005
TL;DR: This paper presents a new method for determining the orientation of subjective terms based on the quantitative analysis of the glosses of such terms given in on-line dictionaries, and on the use of the resulting term representations for semi-supervised term classification.
Abstract: Sentiment classification is a recent subdiscipline of text classification which is concerned not with the topic a document is about, but with the opinion it expresses. It has a rich set of applications, ranging from tracking users' opinions about products or about political candidates as expressed in online forums, to customer relationship management. Functional to the extraction of opinions from text is the determination of the orientation of ``subjective'' terms contained in text, i.e. the determination of whether a term that carries opinionated content has a positive or a negative connotation. In this paper we present a new method for determining the orientation of subjective terms. The method is based on the quantitative analysis of the glosses of such terms, i.e. the definitions that these terms are given in on-line dictionaries, and on the use of the resulting term representations for semi-supervised term classification. The method we present outperforms all known methods when tested on the recognized standard benchmarks for this task.

416 citations


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

  • ...A semi-supervised learning based method to classify terms as per their semantic orientation, was proposed by Esuli and Sebastiani (2005)....

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Journal ArticleDOI
TL;DR: The development of Sentiment Analysis and Opinion Mining during the last years are reviewed, and the evolution of a relatively new research direction is discussed, namely, Contradiction Analysis.
Abstract: In the past years we have witnessed Sentiment Analysis and Opinion Mining becoming increasingly popular topics in Information Retrieval and Web data analysis. With the rapid growth of the user-generated content represented in blogs, wikis and Web forums, such an analysis became a useful tool for mining the Web, since it allowed us to capture sentiments and opinions at a large scale. Opinion retrieval has established itself as an important part of search engines. Ratings, opinion trends and representative opinions enrich the search experience of users when combined with traditional document retrieval, by revealing more insights about a subject. Opinion aggregation over product reviews can be very useful for product marketing and positioning, exposing the customers' attitude towards a product and its features along different dimensions, such as time, geographical location, and experience. Tracking how opinions or discussions evolve over time can help us identify interesting trends and patterns and better understand the ways that information is propagated in the Internet. In this study, we review the development of Sentiment Analysis and Opinion Mining during the last years, and also discuss the evolution of a relatively new research direction, namely, Contradiction Analysis. We give an overview of the proposed methods and recent advances in these areas, and we try to layout the future research directions in the field.

414 citations