The challenge is not to produce an ideal product, but rather to provide a framework for the analysis of texts that is useful in most situations. The product has to be easy to use and to read, yet effective and precise with the information it provides. There are several common challenges. The first is, of course, that the data needs to be analyzed correctly. The sentiment analysis algorithm needs to determine which words in the text are positive or negative. The algorithm also needs to determine how many of those particular words are used together in the text. Second, the algorithm needs to take into account the context of the text and the meaning of what is being expressed. For example, in the example above: This is not working should be considered a negative signal because it suggests something wrong with the operation. However, if This is working is the text, it is only a negative signal because This is negative (Alaei et al., 2019). On the other hand, if This is working were the context: This is working is positive because it suggests that there is something good about the operation.
The first is in the interpretation of the observed data. This is where sentiment analysis is more challenging than the classification problem. Another is in the interpretation of a complex sentiment. The most popular application area is based on different kinds of applications for sentiment analysis. The most valuable application areas are based on the number of people paying attention to the content by paying attention to the topics. Most of the sentiment analysis applications aim to help us understand whether an application behaves uncertainly. The first such application was designed to evaluate Google’s search engine. The uncertain application is designed to assess which of the thousands of Google search results are most relevant to one search query (Sharda et al., 2020). The second application assesses the effectiveness of a particular product from a consumer point of view.
Alaei, A. R., Becken, S., & Stantic, B. (2019). Sentiment analysis in tourism: capitalizing on big data. Journal of Travel Research, 58(2), 175-191.
Sharda, R., Delen, Dursun, and Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. 11th Edition. By PEARSON Education. Inc.