DOCUMENT SENTIMENT ANALYSIS USING OPINION MINING
I. FORMAL INTRODUCTION
Sentiment analysis is becoming increasingly important in text mining and natural language processing research (NLP). There has been an increase in the accessibility of online applications, as well as an increase in social platforms for opinion sharing, online review websites, and personal blogs, which have piqued the interest of stakeholders such as customers, organizations, and governments to analyze and explore these opinions.
As a result, the primary role of sentiment classification is to analyze an online document such as a blog, comment, review, or new item as a whole and categorize it as positive, negative, or neutral [1], [2]. The study of sentimental analysis has recently gained popularity among researcher scholars, and a number of research studies are being conducted on the subject.
It is also known as sentiment classification and opinion mining. The sentimental analysis is composed of text classification and the separation of sentiments for subjective texts, which are primarily related to consumer reviews on products and services. Positive and negative sentiments are the two types of sentiments. In some cases, there may be no sentiments, which is referred to as neutral.
Sentiment analysis is a complex process that includes several tasks such as sentiment analysis (SA), subjectivity analysis, opinion mining (OM), and sentiment orientation. It is regarded as a novel and developing new research field in machine learning (ML), natural language processing (NLP), and computational linguistics. The sentiment analysis is divided into three levels: word level, sentence level, and document level. The task required for the process is determined by the level of analysis.
Because of the difficulty in carrying out the analysis, the word level is the most complex, whereas the analysis is simpler at the sentence and document levels [3]. The two main techniques used for sentimental analysis review are semantic-based analysis and machine learning. In addition, a method is used to combine both techniques. There have been numerous studies that have used the machine-learning technique [4]-[10].
A Semantic-based analysis is a well-known sentiment analysis technique [11], [12]. The remainder of this paper is organized as follows: The following section goes over sentiment analysis and opinion mining. Following that, different levels of classifying sentiments are presented.
Section IV contains a description of previous work done on sentiment analysis techniques. Section V also includes the sentiment analysis resources. Section VI discusses the difficulties in sentiment analysis. Finally, section VII declares the conclusion of this review.
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DOCUMENT SENTIMENT ANALYSIS USING OPINION MINING
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