摘要
Huge amount of data is being produced every second for microblogs,different content sharing sites,and social networking.Sentimental classification is a tool that is frequently used to identify underlying opinions and sentiments present in the text and classifying them.It is widely used for social media platforms to find user’s sentiments about a particular topic or product.Capturing,assembling,and analyzing sentiments has been challenge for researchers.To handle these challenges,we present a comparative sentiment analysis study in which we used the fine-grained Stanford Sentiment Treebank(SST)dataset,based on 215,154 exclusive texts of different lengths that are manually labeled.We present comparative sentiment analysis to solve the fine-grained sentiment classification problem.The proposed approach takes start by pre-processing the data and then apply eight machine-learning algorithms for the sentiment classification namely Support Vector Machine(SVM),Logistic Regression(LR),Neural Networks(NN),Random Forest(RF),Decision Tree(DT),K-Nearest Neighbor(KNN),Adaboost and Naïve Bayes(NB).On the basis of results obtained the accuracy,precision,recall and F1-score were calculated to draw a comparison between the classification approaches being used.
