In the world of social media, the amount of textual data is increasing exponentially on the internet, and a large portion of it expresses subjective opinions. Sentiment Analysis (SA) also named as Opinion mining, which is used to automatically identify and extract the subjective sentiments from text. In recent years, the research on sentiment analysis started taking off because of a huge of amount of data is available on the social media like twitter, machine learning algorithms popularity is increased in IR (Information Retrieval) and NLP (Natural Language Processing). In this work, we proposed three phase systems for sentiment classification in twitter tweets task of SemEval competition. The task is predicting the sentiment like negative, positive or neutral of a twitter tweets by analyzing the whole tweet. The first system used Artificial Bee Colony (ABC) optimization technique is used with Bag-of-words (BoW) technique in association with Naive Bayes (NB) and k-Nearest Neighbor (kNN) classification techniques with combination of various categories of features in identifying the sentiment for a given twitter tweet. The second system used to preserve the context a Rider Feedback Artificial Tree Optimization-enabled Deep Recurrent neural networks (RFATO-enabled Deep RNN) is developed for the efficient classification of sentiments into various grades. Further to improve the accuracy of classification on n-valued scale Adaptive Rider Feedback Artificial Tree (Adaptive RiFArT)-based Deep Neuro fuzzy network is devised for efficient sentiment grade classification. Finally, this research work proposed a Fuzzy-Rule Based Deep Sentiment Extraction (FBDSE) Algorithm with Deep Sentiment Score computation. Accuracy measure is considered to test the proposed systems performance. It was observed that the fuzzy-rule based system achieved good accuracy compared with machine learning and deep learning based approaches.
Purpose Internet has endorsed a tremendous change with the advancement of the new technologies. The change has made the users of the internet to make comments regarding the service or product. The Sentiment classification is the process of analyzing the reviews for helping the user to decide whether to purchase the product or not. Design/methodology/approach A rider feedback artificial tree optimization-enabled deep recurrent neural networks (RFATO-enabled deep RNN) is developed for the effective classification of sentiments into various grades. The proposed RFATO algorithm is modeled by integrating the feedback artificial tree (FAT) algorithm in the rider optimization algorithm (ROA), which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of term frequency-inverse document frequency (TF-IDF) features and spam words-based features are extracted from the review data to form the feature vector. Feature fusion is performed based on the entropy of the features that are extracted. The metrics employed for the evaluation in the proposed RFATO algorithm are accuracy, sensitivity, and specificity. Findings By using the proposed RFATO algorithm, the evaluation metrics such as accuracy, sensitivity and specificity are maximized when compared to the existing algorithms. Originality/value The proposed RFATO algorithm is modeled by integrating the FAT algorithm in the ROA, which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of TF-IDF features and spam words-based features are extracted from the review data to form the feature vector. Feature fusion is performed based on the entropy of the features that are extracted.
Sentiment analysis is an efficient technique for expressing users’ opinions (neutral, negative or positive) regarding specific services or products. One of the important benefits of analyzing sentiment is in appraising the comments that users provide or service providers or services. In this work, a solution known as adaptive rider feedback artificial tree optimization-based deep neuro-fuzzy network (RFATO-based DNFN) is implemented for efficient sentiment grade classification. Here, the input is pre-processed by employing the process of stemming and stop word removal. Then, important factors, e.g. SentiWordNet-based features, such as the mean value, variance, as well as kurtosis, spam word-based features, term frequency-inverse document frequency (TF-IDF) features and emoticon-based features, are extracted. In addition, angular similarity and the decision tree model are employed for grouping the reviewed data into specific sets. Next, the deep neuro-fuzzy network (DNFN) classifier is used to classify the sentiment grade. The proposed adaptive rider feedback artificial tree optimization (A-RFATO) approach is utilized for the training of DNFN. The A-RFATO technique is a combination of the feedback artificial tree (FAT) approach and the rider optimization algorithm (ROA) with an adaptive concept. The effectiveness of the proposed A-RFATO-based DNFN model is evaluated based on such metrics as sensitivity, accuracy, specificity, and precision. The sentiment grade classification method developed achieves better sensitivity, accuracy, specificity, and precision rates when compared with existing approaches based on Large Movie Review Dataset, Datafiniti Product Database, and Amazon reviews.
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