Sentiment Analysis Techniques for Social Media Data: A Review

Sharma, Dipti Misra; Sabharwal, Munish; Goyal, Vinay; Vij, Mohit

doi:10.1007/978-981-15-0029-9_7

Cited by 30 publications

(23 citation statements)

References 30 publications

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“…in which p(class) is the prior probability of the class in the dataset, P (features| class) is the prior probability of a feature related to a class, and p(feature) is the prior probability of a feature that has occurred [29].…”

Section: Online Reviewsmentioning

confidence: 99%

“…It builds a hyperplane that acts as a separator to designate the decision boundaries between data points with different classes (labels). The best hyperplane is one that can maintain the maximum distance between two support vectors of different classes [29]. SVM builds a hyperplane that maximizes the functional margin to the nearest training data points of any class to perform classification.…”

Section: Online Reviewsmentioning

confidence: 99%

“…The condition is based on the presence or absence of a word. The tree recursively continues the division process until terminal nodes represent the small numbers of features that belong only to one class [29].…”

Section: Online Reviewsmentioning

confidence: 99%

See 2 more Smart Citations

Sentiment Analysis towards Actionable Intelligence via Deep Learning

Eletter¹

2020

TEM Journal

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The exponential growth of unstructured data and the ability of businesses to utilize such data in decision-making have led to competitive advantages. The knowledge provided by analyzing unstructured data is crucial for product developers or service providers because it might affect the sustainability of the business. Sentiment analysis is used to gain an understanding of the attitudes, opinions, and emotions expressed within an online review. Naïve Bayes (NB), logistic regression (LR), decision trees (DT), deep learning (DL), and support vector machines (SVM) were used to build a classification model. In the data mining settings, the classification accuracy is the best metric to highlight the best classifier. The DL classifier outperformed other models in terms of accuracy rate. Classifying customers' feelings toward a product or service is critical for providing actionable insights. Utilizing such models will help to analyze huge volumes of reviews, saving both time and costs.

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Section: Online Reviewsmentioning

confidence: 99%

Section: Online Reviewsmentioning

confidence: 99%

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Sentiment Analysis towards Actionable Intelligence via Deep Learning

Eletter¹

2020

TEM Journal

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“…along with various features like word n-grams, Part-of-speech (POS) tags and specific data regarding tweets such as capital words, emoticons, hashtags, and retweet, etc. Sharma, D. et al [13] presents a review on Sentiment analysis technique for social media data where they also concluded that SVM is the most frequently used algorithm for sentiment analysis.…”

Section: Dipti Sharma Munish Sabharwalmentioning

confidence: 99%

Sentiment Analysis for Social Media using SVM Classifier of Machine Learning

2019

IJITEE

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Sentiment analysis is an area of natural language processing (NLP) and machine learning where the text is to be categorized into predefined classes i.e. positive and negative. As the field of internet and social media, both are increasing day by day, the product of these two nowadays is having many more feedbacks from the customer than before. Text generated through social media, blogs, post, review on any product, etc. has become the bested suited cases for consumer sentiment, providing a best-suited idea for that particular product. Features are an important source for the classification task as more the features are optimized, the more accurate are results. Therefore, this research paper proposes a hybrid feature selection which is a combination of Particle swarm optimization (PSO) and cuckoo search. Due to the subjective nature of social media reviews, hybrid feature selection technique outperforms the traditional technique. The performance factors like f-measure, recall, precision, and accuracy tested on twitter dataset using Support Vector Machine (SVM) classifier and compared with convolution neural network. Experimental results of this paper on the basis of different parameters show that the proposed work outperforms the existing work

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“…In particular, SVM, NB, DTs, RF, and LR methods, etc. which are used extensively with high accuracy in wide application fields that include sentiment analysis, such as cyberhate detection [19] movie and product reviews [20], [21], abusive language detection [22], cyberbullying identification [23], and social media [24]. In addition to classical ML algorithms as presented earlier, there are likewise DL algorithms such as CNN, FFNN, LSTM, GRU, and RNN, which are presently preferred for sentiment classification.…”

mentioning

confidence: 99%

Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

et al. 2021

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At present, with the growing number of Web 2.0 platforms such as Instagram, Facebook, and Twitter, users honestly communicate their opinions and ideas about events, services, and products. Owing to this rise in the number of social platforms and their extensive use by people, enormous amounts of data are produced hourly. However, sentiment analysis or opinion mining is considered as a useful tool that aims to extract the emotion and attitude from the user-posted data on social media platforms by using different computational methods to linguistic terms and various Natural Language Processing (NLP). Therefore, enhancing text sentiment classification accuracy has become feasible, and an interesting research area for many community researchers. In this study, a new Fuzzy Deep Learning Classifier (FDLC) is suggested for improving the performance of data-sentiment classification. Our proposed FDLC integrates Convolutional Neural Network (CNN) to build an effective automatic process for extracting the features from collected unstructured data and Feedforward Neural Network (FFNN) to compute both positive and negative sentimental scores. Then, we used the Mamdani Fuzzy System (MFS) as a fuzzy classifier to classify the outcomes of the two used deep (CNN+FFNN) learning models in three classes, which are: Neutral, Negative, and Positive. Also, to prevent the long execution time taking by our hybrid proposed FDLC, we have implemented our proposal under the Hadoop cluster. An experimental comparative study between our FDLC and some other suggestions from the literature is performed to demonstrate our offered classifier's effectiveness. The empirical result proved that our FDLC performs better than other classifiers in terms of true positive rate, true negative rate, false positive rate, false negative rate, error rate, precision, classification rate, kappa statistic, F1-score and time consumption, complexity, convergence, and stability.

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Sentiment Analysis Techniques for Social Media Data: A Review

Cited by 30 publications

References 30 publications

Sentiment Analysis towards Actionable Intelligence via Deep Learning

Sentiment Analysis towards Actionable Intelligence via Deep Learning

Sentiment Analysis for Social Media using SVM Classifier of Machine Learning

Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier

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