Commit cc1cd1c3 authored by Hetvi Ariwala's avatar Hetvi Ariwala
Browse files

References have been added to the report file.

parent 49a42633
Loading
Loading
Loading
Loading
+1.23 KiB (5.29 MiB)

File changed.

No diff preview for this file type.

+1.54 KiB (40.2 KiB)

File changed.

No diff preview for this file type.

+11 −0
Original line number Diff line number Diff line
@@ -217,4 +217,15 @@ Sentiment analysis using predefined word lists faces the challenge of context in

In summary, the sentiment analysis on the review's dataset has revealed the depth and complexity of user opinions. Alignment exists between manual and automated analysis methods, but each has inherent difficulties in accurately interpreting sentiment, as evidenced by the NRC emotion analysis which depicts a primarily positive sentiment combined with a spectrum of other emotions. Moreover, time-series analysis indicates that user sentiments are not stable, highlighting the dynamic nature of player feedback. However, this analysis also uncovers challenges such as context insensitivity and linguistic evolution, which can lead to misunderstandings. This underscores the need for sentiment analysis tools to evolve in complexity and adaptability to truly reflect the nuanced sentiment of user-generated content. 

\section*{References}

 R Core Team (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/\\

Minqing Hu and Bing Liu. "Mining and summarizing customer reviews."
Proceedings of the ACM SIGKDD International Conference on Knowledge
Discovery \& Data Mining (KDD-2004, full paper), Seattle, Washington,
USA, Aug 22-25, 2004\\

Brewer, Cynthia A. "ColorBrewer 2.0: Color Advice for Cartography." Pennsylvania State University, n.d., https://colorbrewer2.org/

\end{document}