A document-level sentiment analysis approach using artificial neural network and sentiment lexicons
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 intrinsic subjectivity knowledge available in the sentiment lexicons.
Abstract: The abundance of discussion forums, Weblogs, e-commerce portals, social networking, product review sites and content sharing sites has facilitated flow of ideas and expression of opinions. The user-generated text content on Internet and Web 2.0 social media can be a rich source of sentiments, opinions, evaluations, and reviews. Sentiment analysis or opinion mining has become an open research domain that involves classifying text documents based on the opinion expressed, about a given topic, being positive or negative. 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 intrinsic subjectivity knowledge available in the sentiment lexicons. The results obtained from experiments on the movie and hotel review corpora have shown that the proposed approach has been able to reduce dimensionality, while producing accurate results for sentiment based classification of text.
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TL;DR: The vision of digital phenotyping of mental health (DPMH) is outlined by fusing the enriched data from ubiquitous sensors, social media and healthcare systems, and a broad overview of DPMH from sensing and computing perspectives is presented.
88Â citations
TL;DR: Among the two neural network approaches used, probabilistic neural networks (PNNs) outperform in classifying the sentiment of the product reviews and the integration of neural network based sentiment classification methods with principal component analysis (PCA) as a feature reduction technique provides superior performance in terms of training time.
Abstract: The aim of sentiment classification is to efficiently identify the emotions expressed in the form of text messages. Machine learning methods for sentiment classification have been extensively studied, due to their predominant classification performance. Recent studies suggest that ensemble based machine learning methods provide better performance in classification. Artificial neural networks (ANNs) are rarely being investigated in the literature of sentiment classification. This paper compares neural network based sentiment classification methods (back propagation neural network (BPN), probabilistic neural network (PNN) & homogeneous ensemble of PNN (HEN)) using varying levels of word granularity as features for feature level sentiment classification. They are validated using a dataset of product reviews collected from the Amazon reviews website. An empirical analysis is done to compare results of ANN based methods with two statistical individual methods. The methods are evaluated using five different quality measures and results show that the homogeneous ensemble of the neural network method provides better performance. Among the two neural network approaches used, probabilistic neural networks (PNNs) outperform in classifying the sentiment of the product reviews. The integration of neural network based sentiment classification methods with principal component analysis (PCA) as a feature reduction technique provides superior performance in terms of training time also.
78Â citations
Cites background from "A document-level sentiment analysis..."
...The BPN training pseudo code is summarized as follows (Sharma and Dey, 2012)....
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...But the state of the art technique for neural network based text sentiment classification are found to be rare from the literature (Zhu et al., 2010; Chen et al., 2011; Sharma and Dey, 2012; Moraes et al., 2013)....
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TL;DR: This work proposes a hybrid machine learning approach to enhance sentiment analysis; as it builds a classification model based on three classes, which are positive, neutral, and negative emotions, using Support Vector Machines (SVM) classifier, while combining two feature selection techniques using the ReliefF and Multi-Verse Optimizer algorithms.
Abstract: Sentiment Analysis is currently considered as one of the most attractive research topics in Natural Language Processing (NLP) field. The main objective of sentiment analysis is to identify the opinions and emotions of the users through written contents. While there are different studies that have approached this field using various techniques, it is still considered a challenging topic with many difficulties that are yet to be solved, such as having modern accents, slang words, spelling and grammatical mistakes, and other issues that cannot be overcome with traditional methods and sentiment lexicons. In this work, we propose a hybrid machine learning approach to enhance sentiment analysis; as we build a classification model based on three classes, which are positive, neutral, and negative emotions, using Support Vector Machines (SVM) classifier, while combining two feature selection techniques using the ReliefF and Multi-Verse Optimizer (MVO) algorithms. We also extract more than 6900 tweets from Twitter social network to test our work. Our hybrid method is compared against other classifiers and methods in terms of accuracy. Results show that our proposed method outperforms other techniques and classifiers, by obtaining better results in most of the datasets while reducing the number of features by up to 96.85% from the original feature set. We also categorize the extracted features into Objective, Subjective and Emoticon words to analyze them during the first and the final feature selection processes and find any existing relations. Very similar results are obtained by both feature selection techniques; due to a number of factors that are explained in this paper.
51Â citations
TL;DR: The results demonstrate that neural network based opinion mining approach outperforms the support vector machine method in terms of precision, recall and f-score, and it is shown that the performance of radial basis function neuralnetwork method is superior than probabilistic neural network method in Terms of the performance measures used.
38Â citations
Additional excerpts
...…the advance in neural network ethodology, like fast training algorithm for deep multilayer eural networks (Chen, Liu, & Chiu, 2011; Ghiassi, Skinner, Zimbra, 2013; Jian et al., 2010; Luong, Socher, & Manning, 013; Moraes, Valiati, & Neto, 2013; Noferesti & Shamsfard, 015; Sharma & Dey, 2012)....
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TL;DR: Text is a typical example of unstructured and heterogeneous data in which massive useful knowledge is embedded and sentiment analysis is used to analyze and predict sentiment polarities of the text.
Abstract: Text is a typical example of unstructured and heterogeneous data in which massive useful knowledge is embedded. Sentiment analysis is used to analyze and predict sentiment polarities of the text. T...
28Â citations
References
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Book•
01 Jan 2020TL;DR: In this article, the authors present a comprehensive introduction to the theory and practice of artificial intelligence for modern applications, including game playing, planning and acting, and reinforcement learning with neural networks.
Abstract: The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. For computer professionals, linguists, and cognitive scientists interested in artificial intelligence.
16,983Â citations
17 Jul 1986
15,313Â citations
Book•
08 Jul 2008TL;DR: This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems and focuses on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis.
Abstract: An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.
This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.
7,452Â citations
"A document-level sentiment analysis..." refers background in this paper
...BPANNs have rarely been considered for sentiment analysis tasks as can be verified from recent surveys on sentiment analysis [23, 35]....
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...reviews) according to their polarity (positive or negative) [23]....
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...0 mediums like message forums, blogs or reviews sites to express their opinion and access opinions expressed by others [23, 35]....
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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.
7,330Â citations
"A document-level sentiment analysis..." refers background or methods in this paper
...The semantic approach uses different kinds of semantic relationships between words like synonyms and antonyms, which may be used to calculate sentiment polarities [17]....
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...Opinionated sentences were identified using dictionary of adjectives (sentiment lexicon) [17]....
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...Positive and negative sentiment based summaries for product features from reviews were proposed by Hu and Liu (2004)....
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...The dictionary based approach utilizes a pre-built dictionary known as sentiment lexicons (such as the General Inquirer [35] and Opinion Lexicons [17]) that contains opinion polarities of words....
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...The Opinion Lexicon is adopted from [17]....
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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.
6,980Â citations