Sentiment Analysis as a Restricted NLP Problem

Sentiment Analysis as a Restricted NLP Problem

Akshi Kumar (Delhi Technological University, India) and Divya Gupta (Galgotias University, India)
Copyright: © 2021 |Pages: 32
DOI: 10.4018/978-1-7998-4240-8.ch004


With the accelerated evolution of social networks, there is a tremendous increase in opinions by the people about products or services. While this user-generated content in natural language is intended to be valuable, its large amounts require use of content mining methods and NLP to uncover the knowledge for various tasks. In this study, sentiment analysis is used to analyze and understand the opinions of users using statistical approaches, knowledge-based approaches, hybrid approaches, and concept-based ontologies. Unfortunately, sentiment analysis also experiences a range of difficulties like colloquial words, negation handling, ambiguity in word sense, coreference resolution, which highlight another perspective emphasizing that sentiment analysis is certainly a restricted NLP problem. The purpose of this chapter is to discover how sentiment analysis is a restricted NLP problem. Thus, this chapter discussed the concept of sentiment analysis in the field of NLP and explored that sentiment analysis is a restricted NLP problem due to the sophisticated nature of natural language.
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With the emergence of WWW and the Internet, the interest of social media has increased tremendously over the past few years. This new wave of social media has generated a boundless amount of data which contains the emotions, feelings, sentiments or opinions of the users. This abundant data on the web is in the form of micro-blogs, web journals, posts, comments, audits and reviews in the Natural Language. The scientific communities and business world are utilizing this user opinionated data accessible on various social media sites to gather, process and extract the learning through natural language processing. In this way, there is a need to detect and distinguish the sentiments, attitudes, emotions and opinions of the users from the user’s generated content. Sentiment Analysis is the process which aids to recognize and classify the emotions and opinions of users in the communicated information, in order to determine whether the opinion of the user towards a specific service or product is positive, negative or neutral through NLP, computational linguistics and text analysis. While this user opinionated data is intended to be useful, the bulk of this data requires preprocessing and text mining techniques for the evaluation of sentiments from the text written in natural language. Sentiment Analysis permits organizations to trace their brand reception and popularity, enquire about new product perception and anticipation by the consumers, improve customer relation models, enquire company reputation in the eyes of customers and to track the stock market. According to the Local consumer review survey (Bloem, 2017), 84 percent of the total people trust online reviews as much as a personal recommendation given to them. Thus, it is important to mine online reviews to determine the hidden sentiments behind them.

According to Techopedia (2014), Sentiment Analysis is defined as “a type of data mining that measures the inclination of people's opinions through NLP, computational linguistics and text analysis, which are used to extract and analyze subjective information from the Web- mostly social media and similar sources”. The analyzed data measures the consumer's experiences and opinions towards the products, services or proposed schemes and discloses the contextual orientation of the content. Sentiment analysis encounters many challenges due to its analysis process. These challenges become hindrances in examining the precise significance of sentiments and identifying the sentiment polarity. Some of the common challenges faced by sentiment analysis include difficulties in feature extraction, increased complexity in analyzing label opinionated data, the complication in analysis of other regional languages, requirements of world knowledge, increased domain dependency etc. Unfortunately, sentiment analysis also experiences various difficulties due to the sophisticated nature of the natural language that is being used in the user opinionated data. Some of these issues are generated by NLP overheads like colloquial words, coreference resolution, word sense disambiguation and so on. These issues add more difficulty to the process of sentiment analysis and emphasize that sentiment analysis is a restricted NLP problem. Different algorithms have been applied to analyze the sentiments of the user-generated data. The techniques applied to the user-generated data ranges from statistical to knowledge-based techniques. Even hybrid techniques have been used for the sentiment analysis. Various algorithms, as discussed above, have been employed by sentiment analysis to provide good results, but they have their own limitations in providing high accuracy. It is found from the literature that deep learning methodologies are being used for extracting knowledge from huge amounts of content to reveal useful information and hidden sentiments. Many researchers have explored sentiment analysis from various perspectives but none of the work has focused on explaining sentiment analysis as a restricted NLP problem.

Thus, this chapter presents an overview of Sentiment analysis, which is followed by the related work in section 2, then the detailed description of generally employed methodologies and techniques in Sentiment analysis are discussed in Section 3. Section 4 explains the applications of Sentiment Analysis. Section 5 describes the challenges faced by the Sentiment Analysis and then the challenges relevant to NLP are discussed in Section 6. Section 7 explores the solutions and recommendations to resolve the challenges and in the next section, some future research directions have been explored.

Key Terms in this Chapter

Scarce Resource language: Scarce resource languages are those languages that lack text processing resources and only have basic dictionaries. The sentiment analysis of these languages is difficult due to the absence of developed processing tools and resources for these languages.

Word Sense Disambiguation: Word sense disambiguation is described as a process of recognizing the implication of a term with respect to the context of the sentence. This problem arises when the same word can have diverse meanings in various contexts.

Sentiment Analysis: Sentiment Analysis is the technique that aids to recognize and classify the emotions and opinions of users in the communicated information, so that the opinion of the user for a certain utility or commodity can be determined as being positive, negative or neutral through NLP and content analysis.

Natural Language Processing: Natural language processing is a process that deals with the manipulation of natural language (i.e., language used by humans) by utilizing computers and artificial intelligence to process and analyze huge amounts of natural language data for various applications ranging from automatic summarization to disease prediction.

Opinionated Text: Opinionated text can be defined as the text acquired from blogs, social networking sites or any other online portal in which the users have expressed their disposition and point of view towards any particular product or service.

Subjectivity Detection: Subjectivity detection is the process of differentiating between opinionated and non-opinionated phrases.

Sentiment Polarity: Sentiment polarity for an element defines the orientation of the expressed sentiment, i.e., it determines if the text expresses the positive, negative or neutral sentiment of the user about the entity in consideration.

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