IEEE Organizations related to Sentiment Analysis

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Periodicals related to Sentiment Analysis

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Most published Xplore authors for Sentiment Analysis

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Xplore Articles related to Sentiment Analysis

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Using Twitter Sentiment to Forecast the 2013 Pakistani Election and the 2014 Indian Election

IEEE Intelligent Systems, 2015

This column discusses the authors' efforts to project the winner of the 2013 Pakistan and the 2014 Indian prime minister election using social network analysis and methods to create a diffusion estimation model.


Sentiment on social interactions using linear and non-linear clustering

2016 2nd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), 2016

Social media analytics play a major role in e-commerce for extracting the useful information of a product or service. Opinion mining has become the key process of social media analytics. Twitter is a big online social activity platform where millions of people share their opinions. In this paper two clustering techniques, k-means and DBSCAN, are applied to an annotated Twitter ...


Sentiment analysis on product reviews

2017 International Conference on Computing, Communication and Automation (ICCCA), 2017

Sentiment analysis is used for Natural language Processing, text analysis, text preprocessing, Stemming etc. are the major research field in current time. Sentiment analysis using different techniques and tools for analyze the unstructured data in a manner that objective results can be generated from them. Basically, these techniques allow a computer to understand what is being said by humans. Sentiment ...


Attention Visualization of Gated Convolutional Neural Networks with Self Attention in Sentiment Analysis

2018 International Conference on Machine Learning and Data Engineering (iCMLDE), 2018

Deep learning is applied to many research topics; Natural Language Processing, Image Processing, and Acoustic Recognition. In deep learning, neural networks have a very complex and deep structure and it is difficult to discuss why they work well or not. So you have to take a trial-and-error to improve their performances. We develop a mechanism to show how neural networks ...


An intelligent framework for sentiment analysis of text and emotions - A review

2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), 2017

Now day's fast spreading usage of social media, many websites have offer reviews of items like books, cars, mobiles, movies etc. They describe the product in some detail and evaluate them as good/bad, preferred/not preferred, so it is necessary to categorize these reviews in an automated way. Sentiment analysis is one kind of computational technique of Artificial Intelligence. Sentiment analysis ...


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Educational Resources on Sentiment Analysis

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IEEE-USA E-Books

  • Using Twitter Sentiment to Forecast the 2013 Pakistani Election and the 2014 Indian Election

    This column discusses the authors' efforts to project the winner of the 2013 Pakistan and the 2014 Indian prime minister election using social network analysis and methods to create a diffusion estimation model.

  • Sentiment on social interactions using linear and non-linear clustering

    Social media analytics play a major role in e-commerce for extracting the useful information of a product or service. Opinion mining has become the key process of social media analytics. Twitter is a big online social activity platform where millions of people share their opinions. In this paper two clustering techniques, k-means and DBSCAN, are applied to an annotated Twitter dataset in order to evaluate use of clustering for detecting different types of sentiment. Results are very encouraging for DBSCAN but less useful for k-means.

  • Sentiment analysis on product reviews

    Sentiment analysis is used for Natural language Processing, text analysis, text preprocessing, Stemming etc. are the major research field in current time. Sentiment analysis using different techniques and tools for analyze the unstructured data in a manner that objective results can be generated from them. Basically, these techniques allow a computer to understand what is being said by humans. Sentiment analysis uses different techniques to determine the sentiment of a text or sentence. The Internet is a large repository of natural language. People share their thoughts and experiences which are subjective in nature. Many a time, getting suitable information about a product can became tedious for customers. Companies may not be fully aware of customer requirements. Product reviews can be analyzed to understand the sentiment of the people towards a particular topic. However, these are voluminous; therefore a summary of positive and negative reviews needs to be generated. In this paper, the main focus is on the review of algorithms and techniques used for extract feature wise summary of the product and analyzed them to form an authentic review. Future work will include more product reviews websites and will focus on higher level natural language processing tasks. Using best and new techniques or tool for more accurate result in which the system except only those keywords which are in dataset rest of the words are eliminated by the system.

  • Attention Visualization of Gated Convolutional Neural Networks with Self Attention in Sentiment Analysis

    Deep learning is applied to many research topics; Natural Language Processing, Image Processing, and Acoustic Recognition. In deep learning, neural networks have a very complex and deep structure and it is difficult to discuss why they work well or not. So you have to take a trial-and-error to improve their performances. We develop a mechanism to show how neural networks predict final results and help you to design a new neural network architecture based on its prediction criteria. Speaking concrete, we visualize important features to predict the final results with an attentional mechanism. In this paper, we take up sentient analysis, which is one of natural language processing tasks. In image processing visualizing weights of a neural network is a major approach and you can obtain intuitive results; object outlines and object components. However, in natural language processing, the approach is not interpretable because a discriminate function constructed by a neural network is a complex and nonlinear one and it is very difficult to correlate weights and words in a text. We employ Gated Convolutional Neural Network (GCNN) and introduce a self-attention mechanism to understand how GCNN determines sentiment polarities from raw reviews. GCNN can simulate an n-gram model and the self-attention mechanism can make correspondence between weights of a neural network and words clear. In experiments, we used Amazon reviews and evaluated the performance of the proposed method. Especially, the proposed method was able to emphasize some words in the review to determine sentiment polarity. Moreover, when the prediction was wrong, we were able to understand why the proposed method made mistakes because we found what words the proposed method emphasized.

  • An intelligent framework for sentiment analysis of text and emotions - A review

    Now day's fast spreading usage of social media, many websites have offer reviews of items like books, cars, mobiles, movies etc. They describe the product in some detail and evaluate them as good/bad, preferred/not preferred, so it is necessary to categorize these reviews in an automated way. Sentiment analysis is one kind of computational technique of Artificial Intelligence. Sentiment analysis is a task of identifying positive and negative opinion, emotion and evaluation in text available over the social networking websites and the World Wide Web. The sentiment analysis has been gained quite popularity in the recent years. The analysis serves as an important feedback for further improvement in the offered services and user experiences. This paper presents a study and analysis of several methods and tools which have been applied in the sentiment analysis process.

  • A Multi-Sentiment Classifier Based on GRU and Attention Mechanism

    Previous sentiment analysis studies have focused on monolingual texts, and are basically multi-category tasks (ie, a sentence belongs to only one category). However, in practice, a sentence often expresses multiple sentiments, and the text often contains multiple languages. This paper proposes a multi-label sentiment classifier based on GRU and attention mechanism, which has achieved good results in the data set provided by NLP&CC; share task 1.

  • Product rating using sentiment analysis

    Customer feedbacks are the mile stones for the success functionality for the companies. A producer will get the correct result of his product from the customer feedback. He can make necessary changes to his product according to the feedback. But most users always fail to give their feedbacks. To avoid the difficulty of providing feedback, this paper focus on the technique of providing automatic feedback on the basis of data collected from Twitter. These data streams are filtered and analyzed and feedback is obtained through opinion mining. Here we mainly analyze the data for mobile phones. The experiments have shown 80% accuracy in the sentimental analysis. Our framework is able to provide fast, valuable feedbacks to companies.

  • A Corpus for Sentiment Analysis and Emotion Recognition for a Learning Environment

    In this paper we present the creation process of a corpus of phrases (opinions) related to learning computer programming. Opinions (textual phrases) are categorized in different emotions related to learning such as frustration, boring, excitement, and engagement. The results shows that in 851 opinions (754 Positives and 97 Negatives), there was a neutral, excited and engaged emotional tendency which indicates that we must include resources that induce students to write negative comments. We tested the corpus with a set of machine learning classifiers where the classifier with the highest score was Bernoulli Naïve Bayes with an accuracy of 76.77%.

  • Extracting News Sentiment and Establishing Its Relationship with the S&P 500 Index

    Sentiment analysis has been shown to be a useful tool for quantitative analysis in the world of finance. Researchers have shown that the sentiment picked up from the news media can be correlated with movement of the stock market. Here we use the Harvard General Inquirer to determine the sentiment present in Reuter's articles. After first generating positive and negative sentiment data we use the Kalman filter for smoothing. We then establish a correlation between the movement of the S&P; 500 and sentiment. The results indicate that correlations between the sentiment in the news and the S&P; index are strong for five of the seven years analyzed.

  • Competitive Self-Training technique for sentiment analysis in mass social media

    This paper aims to analyze user's emotion automatically by analyzing Twitter using "data without sentiment labels", not only "data with sentiment labels", to increase accuracy of sentiment analysis through an improved Self-Training, one of Semi-supervised learning techniques. Self-Training has a weak point that a classification mistake can reinforce itself. Self-Training iteratively modifies the model based on the output of the model. Thus, if the model generates wrong output, the model can be wrongly modified. For alleviate this weak point, we propose a competitive Self-Training technique. We create three models based on the output of the model and choose the best. Three models are created by binary mixture perspectives: the threshold, the same number, and the maximum number for updates. We repeat step that creating model and choosing a best model highest to get F-measure. Finally, we can improve the performance of sentiment analysis model.



Standards related to Sentiment Analysis

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