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Book ChapterDOI: 10.1007/978-981-10-7868-2_65

Grammar Rule-Based Sentiment Categorization Model for Tamil Tweets

01 Jan 2018-pp 687-695
Abstract: The widespread of social media is growing every day where users are sharing their opinions, reviews, and comments on an item or product. The aim is to develop a model to mine user tweets collected from Twitter. In this paper, our contribution on user tweets to find the sentiments expressed by users about Tamil movies based on the grammar rule. Tamil movies domain is selected to confine our scope of the work. After preprocessing, N-gram approach is applied to classify tweets into different genres. This work intends to find the polarity of Tamil tweets in addition to genre classification. In this work, it is also shown how to collect user tweets which comes as data stream using modified N-gram approach to predict the sentiments of the users in the dataset. Results suggest that N-gram model not only remove the complexity of natural language process but also help to improve the decision-making process.

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Topics: Tamil (54%), Sentiment analysis (54%)
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Proceedings ArticleDOI: 10.1109/ICIIS47346.2019.9063341
01 Dec 2019-
Abstract: Sentiment Analysis (SA) is an application of Natural Language Processing (NLP) to extract the sentiments expressed in the text. In this paper, we experimented five approaches to perform SA, namely, Lexicon based approach, Supervised Machine learning based approach, Hybrid approach, K-means with Bag of Word (BoW) approach and K-modes with BoW approach. We have experimented these approaches using five corpora with different feature representation techniques to predict the best approach to perform SA in Tamil texts. In this research we used Basic features such as word count and punctuation count in addition to traditional features such as Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) included to check their influence in the prediction. We have compared these approaches, features and the corpora. From the evaluation the highest accuracy of 79% is obtained for UJ_Corpus_Opinions_Nouns corpus with fastText for supervised Machine learning based approach.

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Topics: Sentiment analysis (58%), Bag-of-words model (55%), Feature (machine learning) (54%) ...read more

38 Citations


Open accessPosted Content
Abstract: Detecting face quiet have issues in managing pictures in the bare because of huge presence dissimilarities. Rather than exit look differences straightforwardly to measurable knowledge calculations, we suggest a various leveled (body-part) portion centered Essential replica to unequivocally catch them. This replica empowers part subtype alternative to deal with nearby appearance variations, for example, shut and exposed mouth, and body-portion distortion to catch the worldwide look differences, for example, posture and articulation. In recognition, applicant frame is fit to the Essential replica to surmise the portion area and portion subtype, and finding total is then processed in light of the fitted arrangement. Thusly, the impact of appearance variety is diminished. Other than the face replica, we abuse the co-event amongst body and face, which handles huge variations, for example, substantial impediments, to additionally support the face detection execution. We display an expression based portrayal for body detection, and propose an Essential setting replica to together encrypt the yields of body and face detector.

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Topics: Replica (55%), Face detection (54%)

8 Citations


Open accessJournal ArticleDOI: 10.4038/JSC.V9I2.14
31 Dec 2018-Journal of science
Abstract: Sentiment Analysis (SA) is an application of Natural Language Processing (NLP) to analyse the sentiments expressed in the text. It classifies into categories of qualities and opinions such as good, bad, positive, negative, neutral, etc. It employs machine learning techniques and lexicons for the classification. Nowadays, people share their opinions or feelings about movies, products, services, etc. through social media and online review sites. Analysing their opinions is beneficial to the public, business organisations, film producers and others to make decisions and improvements. SA is mostly employed in English language but rare for Indian languages including Tamil. This review paper aims to critically analyse the recent literature in the field of SA with Tamil text. Objectives, Methodologies and success rates are taken in consideration for the review. We shall conclude from the review that SVM and RNN classifiers taking TF-IDF and Word2vec features of Tamil text give better performance than grammar rules based classifications and other classifiers with presence of words, TF and BoW as features.

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Topics: Tamil (60%), Sentiment analysis (59%)

3 Citations


Journal ArticleDOI: 10.1007/S00500-020-05400-X
01 Mar 2021-
Abstract: Mostly sentiment analysis employs dictionary approaches for recognizing the polarity of terms in a review. However, in sentiment analysis between different domains called domain adaptation (DA), the sentiment lexicon disappoints that leads to the feature mismatch problem. Now, many e-commerce sites try to process reviews in their native languages. In this paper, we propose an enhanced dictionary in our native language (Tamil) that aims at building contextual relationships among the terms of multi-domain datasets that tries to minimize the feature mismatch problem. The proposed dictionary employs both labeled and unlabeled data from the source domain and unlabeled data from the target domain. More precisely, the initial dictionary explores pointwise mutual information for calculating contextual weight then the final dictionary estimates the rank score based on the importance of terms among all the reviews. This work intends to classify reviews of multiple target domains in Tamil by using the unified dictionary with a large number of vocabularies. This extendible dictionary significantly improves the accuracy of DA with the other baseline methods and handles many words in multiple domains with ease.

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Open accessJournal Article
Abstract: A precise analysis of the physical properties and laws of the underlying electrodynamic problem has been carried out to confirm the suitability and applicability of hierarchical approaches for circuit simulation. Together with the proposed simulation instructions and guidelines conventional PEEC techniques can be improved considerably. By means of a PEEC formulation in curvilinear coordinates, high flexibility and compatibility to orthogonal discretizations can be achieved. Therequired number of cells can be reduced significantly by applying non-orthogonal discretization. Therefore, this approach enables a kind of model order reduction. In combination with highly-sophisticated quadrature rules for an efficient numerical integration and regarding the proposed validity aspects concerning mesh generation, high demands for simulation accuracy can be met. The proposed reluctance-based PEEC method enables a sparse formulation of element matrices and therefore supports the application of iterative solution methods.

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Topics: Solver (64%)
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Open accessBook
Bo Pang1, Lillian Lee2Institutions (2)
08 Jul 2008-
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.

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7,180 Citations


Open accessProceedings ArticleDOI: 10.1145/1014052.1014073
Minqing Hu1, Bing Liu1Institutions (1)
22 Aug 2004-
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.

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  • Figure 6: Infrequent feature extraction
    Figure 6: Infrequent feature extraction
  • Figure 7: Predicting the orientations of opinion sentences
    Figure 7: Predicting the orientations of opinion sentences
  • Table 1: Recall and precision at each step of feature generation
    Table 1: Recall and precision at each step of feature generation
  • Table 2: Recall and precision of FASTR
    Table 2: Recall and precision of FASTR
  • Table 3: Results of opinion sentence extraction and sentence orientation prediction
    Table 3: Results of opinion sentence extraction and sentence orientation prediction
  • + 2

6,565 Citations


Open accessJournal ArticleDOI: 10.1016/J.JOCS.2010.12.007
Johan Bollen1, Huina Mao1, Xiao-Jun Zeng2Institutions (2)
Abstract: Behavioral economics tells us that emotions can profoundly affect individual behavior and decision-making. Does this also apply to societies at large, i.e. can societies experience mood states that affect their collective decision making? By extension is the public mood correlated or even predictive of economic indicators? Here we investigate whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time. We analyze the text content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital, Kind, and Happy). We cross-validate the resulting mood time series by comparing their ability to detect the public's response to the presidential election and Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values. Our results indicate that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others. We find an accuracy of 87.6% in predicting the daily up and down changes in the closing values of the DJIA and a reduction of the Mean Average Percentage Error by more than 6%. Index Terms—stock market prediction — twitter — mood analysis.

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Topics: Mood (61%), Affect (psychology) (52%)

3,996 Citations


Open accessProceedings Article
01 May 2010-
Abstract: Microblogging today has become a very popular communication tool among Internet users. Millions of users share opinions on different aspects of life everyday. Therefore microblogging web-sites are rich sources of data for opinion mining and sentiment analysis. Because microblogging has appeared relatively recently, there are a few research works that were devoted to this topic. In our paper, we focus on using Twitter, the most popular microblogging platform, for the task of sentiment analysis. We show how to automatically collect a corpus for sentiment analysis and opinion mining purposes. We perform linguistic analysis of the collected corpus and explain discovered phenomena. Using the corpus, we build a sentiment classifier, that is able to determine positive, negative and neutral sentiments for a document. Experimental evaluations show that our proposed techniques are efficient and performs better than previously proposed methods. In our research, we worked with English, however, the proposed technique can be used with any other language.

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Topics: Sentiment analysis (68%), Microblogging (56%)

2,440 Citations


Open accessProceedings ArticleDOI: 10.3115/979617.979640
01 Jan 1997-
Abstract: We identify and validate from a large corpus constraints from conjunctions on the positive or negative semantic orientation of the conjoined adjectives. A log-linear regression model uses these constraints to predict whether conjoined adjectives are of same or different orientations, achieving 82% accuracy in this task when each conjunction is considered independently. Combining the constraints across many adjectives, a clustering algorithm separates the adjectives into groups of different orientations, and finally, adjectives are labeled positive or negative. Evaluations on real data and simulation experiments indicate high levels of performance: classification precision is more than 90% for adjectives that occur in a modest number of conjunctions in the corpus.

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1,005 Citations


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