Adaptive co-training SVM for sentiment classification on tweets

Liu, Shenghua; Li, Fuxin; Li, Fangtao; Cheng, Xueqi; Shen, Huawei

doi:10.1145/2505515.2505569

Cited by 62 publications

(24 citation statements)

References 20 publications

(20 reference statements)

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“…However this technique performed well for the domain of electronic products. [27] Proposed adaptive multiclass SVM model which works with topic adaptive sentiment classifier. The authors focused on non-text features to handle the sparsity of tweets.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Tweets using SVM

Ahmad¹,

Aftab²,

Ali³

2017

IJCA

108

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Community's view and feedback have always proved to be the most essential and valuable resource for companies and organizations. With social media being the emerging trend among everyone, it paves way for unprecedented analysis and evaluation of various aspects for which organizations had to rely on unconventional, time consuming and error prone methods earlier. This technique of analysis directly falls under the domain of "sentiment analysis". Sentiment analysis encompasses the vast field of effective classification of user generated text under defined polarities. There are several tools and algorithms available to perform sentiment detection and analysis including supervised machine learning algorithms that perform classification on the target corpus, after getting trained with training data. Lexical techniques which performs classification on the basis of dictionary based annotated corpus and Hybrid tools which are combination of machine learning and lexicon based algorithms. In this paper we have used Support Vector Machine (SVM) for sentiment analysis in Weka. SVM is one of the widely used supervised machine learning algorithms for textual polarity detection. To analyze the performance of SVM, two pre classified datasets of tweets are used and for comparative analysis, three measures are used: Precision, Recall and F-Measure. Results are shown in the form of tables and graphs.

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Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Tweets using SVM

Ahmad¹,

Aftab²,

Ali³

2017

IJCA

108

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“…Support vector machine analyzes the data, define the decision boundaries and uses the kernels for computation which are performed in input space [15]. The input data are two sets of vectors of size m each.…”

Section: Support Vector Machinementioning

confidence: 99%

Sentiment Analysis of Twitter Data: A Survey of Techniques

Kharde¹,

Sonawane²

2016

IJCA

228

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With the advancement of web technology and its growth, there is a huge volume of data present in the web for internet users and a lot of data is generated too. Internet has become a platform for online learning, exchanging ideas and sharing opinions. Social networking sites like Twitter, Facebook, Google+ are rapidly gaining popularity as they allow people to share and express their views about topics, have discussion with different communities, or post messages across the world. There has been lot of work in the field of sentiment analysis of twitter data. This survey focuses mainly on sentiment analysis of twitter data which is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous and are either positive or negative, or neutral in some cases. In this paper, we provide a survey and a comparative analyses of existing techniques for opinion mining like machine learning and lexicon-based approaches, together with evaluation metrics. Using various machine learning algorithms like Naive Bayes, Max Entropy, and Support Vector Machine, we provide research on twitter data streams.We have also discussed general challenges and applications of Sentiment Analysis on Twitter.

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“…The experiment results indicated that ANN outperformed SVM on movie review data, but the training time of ANN is too long. Liu et al [16] proposed an adaptive multiclass SVM model for sentiment classification of tweets. The initial common sentiment classifier is transferred to a topic-adaptive one by optimization, unlabeled data selection and adaptive feature expansion.…”

Section: Sentiment Classificationmentioning

confidence: 99%

Investigating association rules for sentiment classification of Web reviews

Yuan

Ouyang

Sheng

2014

Journal of Intelligent &Amp; Fuzzy Systems

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Sentiment Classification of web reviews or comments is an important and challenging task in Web Mining and Data Mining due to the increasing social media and e-commerce industry. This paper presents a novel approach using association rules for sentiment classification of web reviews. An optimal classification rule set is generated to abandon the redundant general rule with comparatively lower confidence. In the class label prediction procedure, we proposed a new metric named Maximum Term Weight (MTW) for the evaluation of rules and a multiple metric voting scheme to solve the problem when the covered rules are not adequately confident or not applicable. The final score of a test review depends on the overall contributions of four metrics. Experiments on multiple domain datasets from web site demonstrate that the voting strategy obtains improvements on other rule based algorithms. Another comparison to popular machine learning algorithms also indicates that the proposed method outperforms these strong benchmarks.

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Adaptive co-training SVM for sentiment classification on tweets

Cited by 62 publications

References 20 publications

Sentiment Analysis of Tweets using SVM

Sentiment Analysis of Tweets using SVM

Sentiment Analysis of Twitter Data: A Survey of Techniques

Investigating association rules for sentiment classification of Web reviews

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