Citius: A Naive-Bayes Strategy for Sentiment Analysis on English Tweets

Gamallo, Pablo; García, Marcos

doi:10.3115/v1/s14-2026

Cited by 122 publications

(69 citation statements)

References 4 publications

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“…al. [9] presented variations of Naive Bayes classifiers for detecting polarity of English tweets. Two different variants of Naive Bayes classifiers were built namely Baseline (trained to classify tweets as positive, negative and neutral), and Binary (makes use of a polarity lexicon and classifies as positive and negative.…”

Section: Opinion Holdermentioning

confidence: 99%

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Sentiment Analysis of Twitter Data: A Survey of Techniques

Kharde¹,

Sonawane²

2016

IJCA

238

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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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Section: Opinion Holdermentioning

confidence: 99%

“…It is a probabilistic classifier and can learn the pattern of examining a set of documents that has been categorized [9]. It compares the contents with the list of words to classify the documents to their right category or class.…”

Section: Naive Bayesmentioning

confidence: 99%

Sentiment Analysis of Twitter Data: A Survey of Techniques

Kharde¹,

Sonawane²

2016

IJCA

238

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“…When a less amount of data is considered recurrent neural network performs better than recursive neural networks. Nicholls and Song [12] have explored different methods for feature selection, and a new method for SA was also proposed. The document frequency difference method proposed in this paper was observed to perform better for SA.…”

Section: Literature Surveymentioning

confidence: 99%

A Survey on Business Facilities Analysis

Joshika¹

2017

Asian J Pharm Clin Res

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The objective is to recommend the feature on which a restaurant has to concentrate for becoming a top rated company in the future. The Yelp dataset has been used in this work. The N-gram method is used to find the features from the reviews of the user, reviews are classified using k-means clustering algorithm, and the features are ranked based on the reviews sentiment. The content-based recommendation will be used for making the recommendation of feature that has to be concentrated. The result will contain the feature on which the 1 or more restaurant has to concentrate that increases the chances of becoming a top rated company in the future. This will help a restaurant to concentrate on a specific feature for getting preferred by the customers.

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“…Naive Bayes relies on the assumption of conditional independence among the features, something that is clearly not true here. While Naive Bayes classifiers manage to perform well despite this assumption, a classifier not reliant on this assumption might outperform a Naive Bayes classifier (Gamallo and Garcia, 2014).…”

Section: Naive Bayes Classifiermentioning

confidence: 99%

SWASH: A Naive Bayes Classifier for Tweet Sentiment Identification

Talbot¹,

Acheampong²,

Wicentowski

2015

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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This paper describes a sentiment classification system designed for SemEval-2015, Task 10, Subtask B. The system employs a constrained, supervised text categorization approach. Firstly, since thorough preprocessing of tweet data was shown to be effective in previous SemEval sentiment classification tasks, various preprocessessing steps were introduced to enhance the quality of lexical information. Secondly, a Naive Bayes classifier is used to detect tweet sentiment. The classifier is trained only on the training data provided by the task organizers. The system makes use of external human-generated lists of positive and negative words at several steps throughout classification. The system produced an overall F-score of 59.26 on the official test set.

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Citius: A Naive-Bayes Strategy for Sentiment Analysis on English Tweets

Cited by 122 publications

References 4 publications

Sentiment Analysis of Twitter Data: A Survey of Techniques

Sentiment Analysis of Twitter Data: A Survey of Techniques

A Survey on Business Facilities Analysis

SWASH: A Naive Bayes Classifier for Tweet Sentiment Identification

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