Real time sentiment analysis of tweets using Naive Bayes

Goel, Ankur; Gautam, Jyoti; Kumar, Sitesh

doi:10.1109/ngct.2016.7877424

Cited by 109 publications

(40 citation statements)

References 2 publications

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“…On the basis of results achieved, it can be seen that the proposed technique provides better results and helps in analyzing the short messages in better way. Ankur Goel, et.al (2016) [16] found that the SentiWordNet along with Naïve Bayes can improve the accuracy of the tweets classification The implementation is done in Python with NLTK and the python twitter APIs are used. The final experiment shows the classification accuracy improved to a considerable extent.…”

Section: Related Workmentioning

confidence: 99%

Accuracy in Binary, Ternary and Multi-Class Classification Sentimental Analysis-a Survey

Shakir¹,

Arora²

2018

ijarcs

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Sentiment analysis is nowadays quite a hot topic for research. Since most of the research is been done on the data acquired from the social networking sites mostly twitter and is subsequently classified into binary classification ("positive" and "negative") or the Ternary classification ("positive", "negative", and "neutral"). The Binary and Ternary classification is not going to serve the sole purpose of sentimental analysis. Multi-Class classification can help in getting the essence and core message from the data. Whether it is Binary, Ternary or Multi-Class classification, the main objective always remains the accuracy of finding the actual sentiments. Since ample work has been done on Binary and Ternary classification and the better accuracy has been achieved but in case of Multi-Class classification accuracy is still a challenge. In this paper, we will analyze different machine learning algorithms and techniques that have been used in the sentimental analysis and the accuracy achieved using those algorithms and techniques.

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Section: Related Workmentioning

confidence: 99%

Accuracy in Binary, Ternary and Multi-Class Classification Sentimental Analysis-a Survey

Shakir¹,

Arora²

2018

ijarcs

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“…The NB classifier is widely used for document classification. This method is preferred because of its speed, simplicity, and effectiveness [27]- [28]. The NB classifier, which is based on the Bayes theorem, was created by using training data to estimate the posterior probability of each category of the test data.…”

Section: Nb Classifiermentioning

confidence: 99%

Automatic complaint classification system using classifier ensembles

Ali-Fauzi¹

2018

Telfor J

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Sambat Online is an online complaint system run by the city government of Malang, Indonesia. Because most citizens do not know to which work units (Satuan Kerja Pemerintah Daerah [SKPDs]) their complaints should be sent, the system administrator must manually sort and classify all of the incoming complaints with respect to the appropriate SKPDs. This study empirically evaluated the application of an automated system to replace the manual classification process. The experiments, which used Sambat Online data, involved five individual classification algorithms-Naïve Bayes, Maximum Entropy, K-Nearest Neighbors, Random Forest, and Support Vector Machines-and two ensemble strategies-hard voting and soft voting. The results show that the Multinomial Naïve Bayes classifier achieved the best performance, an 80.7% accuracy value, of the five individual classifiers. The results also indicate that generally all of the ensemble methods performed better than the individual classifiers. Almost all of them had the same accuracy level of 81.2%. In addition, the soft voting strategy had slightly higher accuracy than the hard one when all five classifiers were used. However, when the three best classifier combinations were used, both had the same level of accuracy.

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“…Numerous research works has been already done in field of sentiment analysis. But the informal tone of tweets has always been a challenge for the analysis [4].…”

Section: Introductionmentioning

confidence: 99%

Naïve Bayes SentimentAnalysis with Fixed and Variable Length Classes Training Data Sets

2019

Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering

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The tremendous development in technology has led to the increasing number of people that join social networks to share information, opinion and so on. With these developments, the social networks are big targets and easy place to capture many people's opinions about certain things. A lot of works have been done by many researchers on the extraction of sentiments from various data sources. Different works employed differenttechniques and approaches. This particular work investigates the training dataset input. We categorized the training datasets into two (2) and termed them; the Variable Length/size (VS) training dataset and the Fixed Lengths/size (FS) training datasets. In the FS, we took the number of positivedocuments equals the number of negative documents. In the VS, we took the number of positivedocuments greater than the number of negative documents (VS positive) and vice versa (VS negative). Binary Naï ve Bayes algorithm was used to test the FS, VS positive and VS negative training datasets on the test dataset. The results showed that, it is better to use the FS training dataset, and if the numbers of positive and negative texts are going to be unequal, then the ratio number of one class to the other should be very small. We can conclusively say that, the wider the ratio the less accurate results, and the narrower the ratio, the more accurate the results.

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Real time sentiment analysis of tweets using Naive Bayes

Cited by 109 publications

References 2 publications

Accuracy in Binary, Ternary and Multi-Class Classification Sentimental Analysis-a Survey

Accuracy in Binary, Ternary and Multi-Class Classification Sentimental Analysis-a Survey

Automatic complaint classification system using classifier ensembles

Naïve Bayes SentimentAnalysis with Fixed and Variable Length Classes Training Data Sets

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