Problem Of Opinion Mining Research Articles

Sentiment Analysis Of Lecturer's Learning Based On Student's Criticism Data Using Naive Bayes Method

The quality of lecturers' teaching is the main key to the success of education at the University. By having lecturers who can provide good teaching, the seeds of nation-changing students can be created. To get lecturers who meet academic standards, of course, evaluation needs to be done. Therefore, Budi Luhur University always conducts questionnaires containing suggestion criticism forms that can be filled in by students. Completion of the suggestion criticism form is carried out for each group of courses at the end of the semester. Currently, the feedback data has not been used to analyze and evaluate the learning process. Therefore, in this research, sentiment analysis is carried out on the results of criticism suggestions that have been sent by students, to find out whether the criticism suggestions are positive, negative, or neutral. One of the sentiment analysis methods that can be used to solve opinion mining problems is the Naive Bayes Method. Data collected as many as 10,067 in the span of 1 semester, namely the odd semester of the 2021/2022 academic year. This suggestion criticism data is then preprocessed, and classified using the Naive Bayes method, testing is carried out using the Naive Bayes Classifier program which is made in the PHP programming language, then accuracy is obtained with the Naïve Bayes method in testing 60% - 40% getting an accuracy result of 83.92%, then in split data 70% - 30% getting an accuracy result of 83.26%, then for split data results 80% - 20% getting an accuracy result of 81.96%, obtained positive sentiment results as much as 2484, then negative sentiment as much as 152, and neutral sentiment as much as 267. This research aims to get sentiment results which are then expected to be used as a reference to improve the quality of teaching lecturers at the university and further evaluation.

Sentiment analysis on stock social media for stock price movement prediction

The opinions of other people are an essential piece of information for making informed decisions. With the increase in using the Internet, today the web becomes an excellent source of user’s viewpoints in different domains. However, in one hand, the growing volume of opinionated text and on the other hand, complexity caused by contrast in user opinion, makes it almost impossible to read all of these reviews and make an informed decision. These requirements have encouraged a new line of research on mining user reviews, which is called opinion mining. User’s viewpoints could change during the time, and this is an important issue for companies. One of the most challenging problems of opinion mining is model-based opinion mining, which aim to model the generation of words by modeling their probabilities. In this paper, we address the problem of model-based opinion mining by introducing a part-of-speech graphical model to extract user’s opinions and test it in two different datasets in English and Persian where the Persian dataset is gathered in this paper from Iranian stock market social network. In the prediction of the stock market by this model, we achieved an accuracy better than methods that are using explicit sentiment labels for comments.

Memory networks for fine-grained opinion mining

Fine-grained opinion mining has attracted increasing attention recently because of its benefits for providing richer information compared with coarse-grained sentiment analysis. Under this problem, there are several existing works focusing on aspect (or opinion) terms extraction which utilize the syntactic relations among the words given by a dependency parser. These approaches, however, require additional information and highly depend on the quality of the parsing results. As a result, they may perform poorly on user-generated texts, such as product reviews, tweets, etc., whose syntactic structure is not precise. In this work, we offer an end-to-end deep learning model without any preprocessing. The model consists of a memory network that automatically learns the complicated interactions among aspect words and opinion words. Moreover, we extend the network with a multi-task manner to solve a finer-grained opinion mining problem, which is more challenging than the traditional fine-grained opinion mining problem. To be specific, the finer-grained problem involves identification of aspect and opinion terms within each sentence, as well as categorization of the identified terms at the same time. To this end, we develop an end-to-end multi-task memory network, where aspect/opinion terms extraction for a specific category is considered as a task, and all the tasks are learned jointly by exploring commonalities and relationships among them. We demonstrate state-of-the-art performance of our proposed model on several benchmark datasets.

Sentiment Analysis of Review Data of a Product Using Python

The other name of sentiment analysis is the opinion mining. It’s one of the primary objectives in a Natural Language Processing(NLP). Opinion mining is having a lot of audience lately. In our research we have taken up a prime problem of opinion mining which is theSentiment Polarity Categorization(SPC) that is very influential. We proposed a methodology for the SPC with explanations to the minute level. Apart from theories computations are made on both review standard and sentence standard categorization with benefitting outcomes. Also, the data that is represented here is from the product reviews given on the shopping site called Amazon.

Multi-Feature Segmentation and Cluster based Approach for Product Feature Categorization

At a recent time, the web has become a valuable source of online consumer review however as the number of reviews is growing in high speed. It is infeasible for user to read all reviews to make a valuable or satisfying decision because the same features, people can write it contrary words or phrases. To produce a useful summary of domain synonyms words and phrase, need to be a group into same feature group. We focus on feature-based opinion mining problem and this paper mainly studies feature based product categorization from the number of users generated review available on the different website. First, a multi-feature segmentation method is proposed which segment multi-feature review sentences into the single feature unit. Second part of speech dictionary and context information is used to consider the irrelevant feature identification, sentiment words are used to identify the polarity of feature and finally an unsupervised clustering based product feature categorization method is proposed. Clustering is unsupervised machine learning approach that groups feature that have a high degree of similarity in a same cluster. The proposed approach provides satisfactory results and can achieve 100% average precision for clustering based product feature categorization task. This approach can be applicable to different product.

Lifelong-RL: Lifelong Relaxation Labeling for Separating Entities and Aspects in Opinion Targets.

It is well-known that opinions have targets. Extracting such targets is an important problem of opinion mining because without knowing the target of an opinion, the opinion is of limited use. So far many algorithms have been proposed to extract opinion targets. However, an opinion target can be an entity or an aspect (part or attribute) of an entity. An opinion about an entity is an opinion about the entity as a whole, while an opinion about an aspect is just an opinion about that specific attribute or aspect of an entity. Thus, opinion targets should be separated into entities and aspects before use because they represent very different things about opinions. This paper proposes a novel algorithm, called Lifelong-RL, to solve the problem based on lifelong machine learning and relaxation labeling. Extensive experiments show that the proposed algorithm Lifelong-RL outperforms baseline methods markedly.

A Review of Unsupervised Approaches of Opinion Target Extraction from Unstructured Reviews

Opinion targets identification is an important task of the opinion mining problem. Several approaches have been employed for this task, which can be broadly divided into two major categories: supervised and unsupervised. The supervised approaches require training data, which need manual work and are mostly domain dependent. The unsupervised technique is most popularly used due to its two main advantages: domain independent and no need for training data. This study presents a review of the state of the art unsupervised approaches for opinion target identification due to its potential applications in opinion mining from web documents. This study compares the existing approaches that might be helpful in the future research work of opinion mining and features extraction.

Semantic-Based Unsupervised Hybrid Technique for Opinion Targets Extraction from Unstructured Reviews

Opinion target identification is an important task of opinion mining problem. Several approaches have been employed for this task that can be broadly divided into two major categories: supervised and unsupervised. The supervised approaches require training data which need manual work and are mostly domain dependent. Unsupervised technique is most popularly used due to its two main advantages: domain independent and no need of training data. This paper presents optimization of unsupervised likelihood ratio test (LRT) technique for opinion targets identification from unstructured reviews. The LRT approach basically depends on frequently observed features and hence do not predict infrequent features (IFF). This paper exploits WordNet lexical resource to identify IFF based on semantic relatedness to the frequent features in review documents which improves the overall accuracy of features extraction algorithms. Empirical results show the validity of the proposed technique.

Mining Opinion Targets from Text Documents: A Review

Opinion targets identification is an important task of the opinion mining problem. Several approaches have been employed for this task, which can be broadly divided into two major categories: supervised and unsupervised. The supervised approaches require training data, which need manual work and are mostly domain dependent. The unsupervised technique is most popularly used due to its two main advantages: domain independent and no need for training data. This paper presents a review of the state of the art unsupervised approaches for opinion target identification due to its potential applications in opinion mining from user discourse. This study compares the existing approaches that might be helpful in the future research work of opinion mining and features extraction.

Extract Product Features in Chinese Web for Opinion Mining

In sentiment analysis of product reviews, one important problem is to extract people's opinions based on product features. Through the summary of feature-level opinions, different consumers can choose their favorite products according to the features that they care about. At the same time, manufacturers can also improve the product features based on the opinions. Different words may be used to express the same product feature. In order to form a useful summary, the feature words need to be clustered into different groups based on the similarity. By analyzing the characteristics of Chinese product reviews on the Internet, a novel method based on feature clustering algorithm is proposed to deal with the feature-level opinion mining problems. Particularly, 1) features considered in this paper include not only the explicit features but