A comparative study of Persian sentiment analysis based on different feature combinations

In recent years, the use of internet and correspondingly the number of online reviews, comments and opinions have increased significantly. It is indeed very difficult for humans to read these opinions and classify them accurately. Consequently, there is a need for an automated system to process this big data. In this paper, a novel sentiment analysis framework for Persian language has been proposed. The proposed framework comprises three basic steps: pre-processing, feature extraction, and support vector machine (SVM) based classification. The performance of the proposed framework has been evaluated taking into account different features combinations. The simulation results have revealed that the best performance could be achieved by integrating unigram, bigram, and trigram features

Sentiment Analysis Using Machine Learning Techniques

Before buying a product, people usually go to various shops in the market, query about the product, cost, and warranty, and then finally buy the product based on the opinions they received on cost and quality of service. This process is time consuming and the chances of being cheated by the seller are more as there is nobody to guide as to where the buyer can get authentic product and with proper cost. But now-a-days a good number of persons depend upon the on-line market for buying their required products. This is because the information about the products is available from multiple sources; thus it is comparatively cheap and also has the facility of home delivery. Again, before going through the process of placing order for any product, customers very often refer to the comments or reviews of the present users of the product, which help them take decision about the quality of the product as well as the service provided by the seller. Similar to placing order for products, it is observed that there are quite a few specialists in the field of movies, who go though the movie and then finally give a comment about the quality of the movie, i.e., to watch the movie or not or in five-star rating. These reviews are mainly in the text format and sometimes tough to understand. Thus, these reports need to be processed appropriately to obtain some meaningful information. Classification of these reviews is one of the approaches to extract knowledge about the reviews. In this thesis, different machine learning techniques are used to classify the reviews. Simulation and experiments are carried out to evaluate the performance of the proposed classification methods. It is observed that a good number of researchers have often considered two different review datasets for sentiment classification namely aclIMDb and Polarity dataset. The IMDb dataset is divided into training and testing data. Thus, training data are used for training the machine learning algorithms and testing data are used to test the data based on the training information. On the other hand, polarity dataset does not have separate data for training and testing. Thus, k-fold cross validation technique is used to classify the reviews. Four different machine learning techniques (MLTs) viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), and Linear Discriminant Analysis (LDA) are used for the classification of these movie reviews. Different performance evaluation parameters are used to evaluate the performance of the machine learning techniques. It is observed that among the above four machine learning algorithms, RF technique yields the classification result, with more accuracy. Secondly, n-gram based classification of reviews are carried out on the aclIMDb dataset..

Sentiment analysis using term based method for customers’ reviews in amazon product

Customers’ review in Amazon platform plays an important role for making online purchase decision making, however the reviews are snowballing in E-commerce day by day. The active sharing of customers’ experience and feedback helps to predict the products and retailers’ quality by using natural language processing. This paper will focus on experimental discussion on Amazon products reviews analysis coupled with sentiment analysis using term-based method and N-gram to achieve best findings. The investigation of sentiment analysis on amazon product gain more valuable information on related text to solve problem related services, products information and quality. The analysis begins with data pre-processing of Amazon products reviews then feature extraction with POS tagging and term-based concept. e-Commerce customer’s reviews normally classify different experience into positive, negative and neutral to judge human behavior and emotion towards the purchase products. The major findings discussed in this journal will be using four different classifier and N-grams methods by computing accuracy, precision, recall and F1-Score. TF-IDF method with N-gram shows unigram with Support Vector Machine learning with highest accuracy results for Amazon product customers’ reviews