Sentiment Analysis of Big Data: Methods, Applications, and Open Challenges

Abstract:
The development of IoT technologies and the massive admiration and acceptance of social media tools and applications, new doors of opportunity have been opened for using data analytics in gaining meaningful insights from unstructured information. The application of opinion mining and sentiment analysis (OMSA) in the era of big data have been used a useful way in categorize the opinion into different sentiment and in general evaluating the mood of the public. Moreover, different techniques of OMSA have been developed over the years in different datasets and applied to various experimental settings. In this regard, this study presents a comprehensive systematic literature review, aims to discuss both technical aspect of OMSA (techniques, types) and non-technical aspect in the form of application areas are discussed. Furthermore, the study also highlighted both technical aspect of OMSA in the form of challenges in the development of its technique and non-technical challenges mainly based on its application. These challenges are presented as a future direction for research.
Existing System:
Big Data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze. However, big data is generally defined through the key characteristics of volume, variety, and velocity. Distributing information across many systems is another challenge which is critical in processing huge amounts of data from different datasets in a reasonable period of time. Cloud computing programming is one way to overcome this issue. Prior to the advent of the Internet, many of us relied on friends and families for product or service recommendations, voting views during local elections, or information when buying a product.
Proposed System:
Text mining/analytics are originally conducted for two purposes. The first purpose is to analyse people’s sentiment on an issue or phenomenon. Hence, sentiment analysis goes through a huge amount of textual data to identify people’s attitudes, thoughts, judgments, and emotions on an issue. The second purpose is to assess people’s opinion on a product, person, event, organization, or topic from a user or group of user perspectives. Similar to sentiment analysis, opinion mining is a natural language processing task that employs an algorithmic technique to recognize opinionated content and categorize it into positive, negative, or neutral polarity. Nonetheless, the application of text mining/analytics has been extended to other areas of human computer applications, and the applications are growing with the growth in big data analytics.
The main aims :
To provide a comprehensive systematic literature review and discuss both the technical aspects of OMSA (techniques, types) and non-technical aspects in the form of application areas.
To highlight both technical (related to the development of sentiment technique) and nontechnical challenges (related to the application of Sentiment analysis). These challenges are presented as future research direction.
Conclusion:
The emergence of the significance of sentiment analysis matches the development of social media usage, including reviews, forum blogs, micro-blogs, Facebook, Twitter, and other social networks. Interestingly, presently we have access to a huge amount of opinionated data which can further be used for different methods of analysis. Typically more than 80% of social media data can be monitored for analysis purposes. For instance, a tweet contains a maximum of 140 characters, therefore monitoring software can assign a specific sentiment score to that tweet. That sentiment score represents a semantic judgment to examine whether the tweet seems to be positive, negative, or neutral. Gathering reviews on products and services on the other hand is mainly done using opinion mining. Products and services are considered as entities, and the process of mining opinions usually involves performing opinions of the texts. The trends further indicate that society is more likely to express feelings rather than what they think on social media platforms.
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