Sentiment detection is a computational technique that attempts to uncover the viewpoint of a user towards a specific entity. It aims to determine the contextual polarity of the text contents (such as comments, reviews, tweets, or posts) as positive or negative. Sentiment analysis has shown a remarkable impact on the business community, whereby taking into account the user opinions, the communities can ensure the sustainability of their product or services. The restaurant is one such business where customers' opinions can be utilized to improve the quality of food, environment, and services. To collect excellent services, customers' instinct is to look through the restaurant reviews before visiting it. Therefore, reviewing a restaurant via the internet has become an ecumenical trend. Thus, it is crucial to identify the sentiment of the user reviews to provide satisfactory services. Moreover, there is a broader business interest in developing a Bengali AI tool to identify sentiment from **code-mixed** (Bengali+English) texts. In this regard, we, **[CUET NLP Lab](https://cuetnlp.com/)**, are elated to present an **NLP competition**, as a part of the 2nd International Workshop on Computational Linguistics and Bangla Language Processing 2023.

This competition is open only to students. 
Registration is required to participate in the competition.  [Registration Link](https://forms.gle/sse3t3xALCboYMJD9) 

**Last Date of Registration:** 10th February 2023
Follow the Facebook event page for the updated information: [CLBLP 2023](https://fb.me/e/3a0ueBDL2)


## Competition Information: 
The Competition will be held from 13th February to 17th February 2023.
Participants abide to follow the competition Rules. Violation of any rules will lead to disqualifying the team from the competition.

**Competition Prize Money: Announced Later**

**Important**: All the teams have to publish their notebooks after the end of the competition period.




## Reference

The following paper describes the process of developing a Bengali sentiment analysis framework.

[Springer'20 Paper](https://link.springer.com/chapter/10.1007/978-3-030-73050-5_19)