University of the Cumberlands Sentiment Analysis Essays Responses

I’m working on a computer science writing question and need an explanation to help me study.

Answer 1 :

Sentiment analysis entails mining text, which identifies and extracts subjective information in the source material and helps any organization gain insights into their products or services via online conversation monitoring (Sharda et al., 2020). Sentiment analysis is the most common text classification tool that analyzes an incoming message and tells whether the underlying sentiment is positive, negative, or neutral. Sentiment analysis is closely related to computational linguistics, Natural Language Processing, and text mining. There are several challenges associated with sentiment analysis (Sharda et al., 2020).

First, grouping synonyms makes it difficult for sentiment analysis since, most of the time, a text tends to contain different words with similar meanings. Identifying these words is difficult since they are used in describing similar features. Grouping such words for accuracy becomes a difficult task. Secondly, there are sarcastic sentences where a text may be ironic or sarcastic (Shayaa et al., 2018). Sentences are phrased positively, whereas the meaning is negative makes it difficult for accurate opinion mining. There are also thwarted expectations in some texts where sentences contain mixed contexts, i.e., both positive and negative sentences. A sentence may be negative at the beginning and end up positive at the end. Such sentences make sentiment analysis difficult. Co-reference resolution is also an aspect of sentiment analysis, where identifying a pronoun or a noun is difficult. Coping up with this challenge is important in sentiment analysis and may improve opinion mining (Shayaa et al., 2018).

Sentiment analysis is applicable in diverse fields. It is applicable in government intelligence, whereby it allows automatic analysis of the opinion that people submit about pending policy or government regulations. In politics, sentiment analysis plays a vital role since this field is dominated by sarcasm, quotes, and complex references to people, organizations, and ideas. It is also applicable in brand management, mainly dealing with social media where anyone can post opinions to damage or boost an organizations’ reputation (Shayaa et al., 2018). Other fields where sentiment analysis is applicable are the financial markets to predict the future value of individuals.

References

Sharda, R., Delen, Dursun, and Turban, E. (2020). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support. 11th Edition. By PEARSON Education. Inc.

Shayaa, S., Jaafar, N. I., Bahri, S., Sulaiman, A., Wai, P. S., Chung, Y. W., … & Al-Garadi, M. A. (2018). Sentiment analysis of big data: Methods, applications, and open challenges. IEEE Access, 6, 37807-37827.

Answer 2:

Challenges Solved by Sentiment Analysis

According to Sharda et al. (2020), sentiment analysis is a closely-related concept to natural language processing that entails the analysis of individual opinions regarding a particular phenomenon. It is popular and effective for businesses since it helps them understand their customers’ feelings about their products and helps understand how to improve them and satisfy their customers. Sentiment analysis deals with the challenges that entail understanding the opinions and the individuals associated with the opinions. Sentiment polarity is the key factor that sentiment analysis focuses on understanding the positive, negative, and neutral polarity. Sharda et al. (2020) outline various applications of sentiment analysis. First, it is used to understand customer voice, allowing organizations to manage customer reactions better since it can access a firm’s product and service reviews. In this application, the customer’s voice analyzes all the customer touchpoints to develop intimate relationships with customers and maintain their loyalty. Second, it is applied in the market’s voice to gain competitive intelligence from the stakeholders and customers. Companies also use sentiment analysis in understanding the voice of the employee to increase staff satisfaction. Sentiment analysis is used to understand employees since employees’ impact customer satisfaction. Therefore, by satisfying the employees, there will be a direct effect on the customers since satisfying them makes them deliver effectively. Organizations also use sentiment analysis in brand management since there is broad data from people’s sentiments on the internet that can help an organization understand how to improve its brand. Also, firms and individuals use it in financial markets to predict stock changes and decide on the investments they should make (Sharda et al., 2020).

References

Sharda, R., Delen, D., & Turban, E. (2020). Analytics, data science. & Artificial Intelligence: Systems for Decision Support. Eleventh ed: Pearson.