Response to Discussion

Running head: CHALLENGES IN SENTIMENT ANALYSIS AND POPULAR APPLICATION AREAS 2

CHALLENGES IN SENTIMENT ANALYSIS AND POPULAR APPLICATION AREAS 2

Challenges in Sentiment Analysis and Popular Application Areas

Santosh Shrestha

University of Cumberlands

Business Intelligence - ITS-531

Dr. Steve Hallman

July 22, 2020

Challenges in Sentiment Analysis and Popular Application Areas

Sentiment Analysis is the process that helps to identify and classify the opinions or feelings expressed in opinioned data in order to ascertain whether the attitude of the writer towards a particular service and product is negative, positive, or neutral (Shahnawaz and Astya, 2017). It gets difficult to put somebody's tweets, posts, videos in a dichotomized category of positive, negative, or neutral. Sentiment suggests a settled opinion reflective of one's feelings and deals with unique properties such as positive versus negative, a range of polarity, or range of strength of opinion (Sharda et al., 2020, p. 418). Humans already deal with difficulties in understanding the sarcasm or the intent even in a face to face conversation. To bring this same capacity of reading between the lines in machines is undoubtedly a challenging task. Not only by using the traditional keyword analysis by holistically analyzing the text using natural language processing and data mining to analyze tone and context accurately. Cultural and regional differences in using language, disconnection of facial expressions, figurative expression, misspellings are some of the barriers in sentiment analysis.

Opinion analysis of the financial market and predicting the next dips and surges have been significantly popular. Many believe that the stock market is mostly sentiment-driven, making it anything but rational, especially for short-term stock movements (Sharda et al., 2020, p. 423). If appropriately implemented, the sentiment analysis can help to track and bring competitive advantages for the movers keeping up with the buzz in the market, potentially impacting trading and liquidity. Brand management is one of the most crucial areas where online sentiment is tracked and responded efficiently. Brand management focuses on keeping up with social media where anyone can post opinions that could potentially damage or boost the company's reputation (Sharda et al., 2020, p. 423). Voice of the market and employee is significantly vital for organizations to understand aggregate opinions and trends regarding stakeholders, customers, potential customers, influencers, and employees (Sharda et al., 2020, p. 423). Similarly, areas such as politics and government intelligence can equally take advantage from sentiment analysis as it needs continuous checking on the opinion of the mass.

References

Sharda, R., Delen, D., Turban, E. (2020). Deep learning. Analytics, data science, & artificial

intelligence: Systems for decision support (pp. 418-423). NJ, Pearson.

Shahnawaz and Astya, P. "Sentiment analysis: Approaches and open issues," 2017 International

Conference on Computing, Communication and Automation (ICCCA), Greater Noida,

2017, pp. 154-158, doi: 10.1109/CCAA.2017.8229791.