WEEK-10

  WEEK 10:

Day 1: I started the week by discussing with the mentor regarding the different ways the emojis are stored in byte level with the article written by David de Hilster. 

Day 2 & 3: I continued my week by changing the heir.kb file that was sent by the mentor for the accepting the emoji dict in the NLP engine, which allowed the emojis to be identified by the parsers.

Continued with adding attributes values to the emojis to make it generalized and attr=1 indicates that it is positive and attr=-1 indicates it as negative.

The above image shows the functions that were created with respective changes in their rules also.

Day 4: I spent the day by generalizing the functions too and with the suggestions from mentor, the functions will be generalized even further. And installed WSL and ECL for later work in the upcoming week.

WEEK 9

 WEEK 9:

Day 1: I started the week by creating the functions necessary for generalizing the rules that I created the previous week. I faced few issues while executing them as it took few trials and errors.

Day 2 & 3: I continued my week by solving my issues with the problems assigning the emojis to the knowledge base, and it was such a minor error that I did like in the initial week and yes, it is the hard way to learn the mistakes we do. The knowledge base looks as described below with 

• Player name 
• Emoji Type
• Type of Sentiment

The above image shows the functions that were created with respective changes in their rules also.

Day 4: I spent the day with the article written by the mentor "https://hpccsystems.com/resources/the-hidden-world-of-unicode-from-text-to-emojis-2/", which helped me understanding the representation of emojis in the Unicode UTF-8, as the emojis integration into the NLP++ based on the skin tone and how it was represented in modern world.   

WEEK 8

 WEEK 8:

Day 1: I started the week by creating rules to excise the unnecessary tweets from the text, which needed some work to be done for the day. 

Day 2 & 3: I continued my week by creating the rules for applying the sentiments for the tweets and it was trial and error basis. I tried to focus on creating them general for all the tweets, but was able to make them exact. This needed to refactored for the next steps.

                The above image shows the rule for the user sentiment, which is quite exact for now. 

            The above image shows the rule for the cricket sentiment, which is generated through the cricket terms that are stored in the dictionary created previously. 

            The above image shows the rule for the team support sentiment, where the sentiment values are assigned based on the support received by the team through the emojis. 

   The rules created are exact and needs to be refactored by creating dictionaries for the user sentiments and making a knowledge base for the emojis and the sentiment values separately which can be used in future for evaluation of the final sentiment.

WEEK 7

 WEEK 7:

Day 1&2: I started the week by making a list of sarcasm statements that are generally used for the tweets regarding the team. The list would need to be updated regularly as the views of the individuals vary from time to time, but we are focused at the particular point of time.

    The above dictionary image represents the emoji dictionary that was built 

    The above image shows the player sentiment that was mapped along with the emoji rather than the rule used in the previous week.

Day 3: On meeting with the mentor, during the start of the week and taking his inputs, I continued completing the emoji dictionary as the emoji functionality in NLP++ has been updated, which would be of great help for my continuation of the work. I continued my week by removing the unnecessary tweets by creating general rules for it.