WEEK 6

  WEEK 6:

Day 1&2: I started the week with creating the sentiment values to the tweets, by creating keywords in the dictionaries. The images represent the emotions attached and the corresponding keywords on the right.

      

Day 3: On meeting with the mentor and taking his inputs, the previous work was not the NLP++ was made for as I worked on algorithmic approach which is not the correct one, so I focused on the starter analyzer provided by the mentor and worked on understanding the tweets on how a human would think of them. Later, I started building on the analyzer by adding rules for extracting account tags and non- account hashtags, which are provided below in the screenshots.

    

Day 4: This day I dedicated myself on creating the emoji dictionary and rules for emoji's as the dictionary wasn't supported, but thanks to the mentor who would be providing the option for emoji dictionary which would be of great help. I even updated my player as specified by the mentor. The images below represent the emoji dictionary and rules for player sentiment along, with the player updated lists.

 

WEEK 5

 WEEK 5:

Day 1&2: This week, I started my actual work of the project after completing few weeks of understanding the concepts of NLP++. I started with converting the tweets obtained into txt files that can be used easily for the ongoing work. The figure below depicts the part of the tweets that I was working on.

Day 3: The day went through going through Pedro's work on the emotion analysis that he worked on and tried to understand his way of working with NLP++ and some areas of how he has been using the debugging. Later I worked on creating the dictionary on some of the cricketing terms and the teams that usually play in the Indian Premier League.

 The two images shows the dictionaries made on cricketing terms and the teams played in the league.

Day 4: This day I dedicated myself on creating the huge list of players played for the team of Royal Challengers of Bangalore(RCB). I found it difficult while creating this list where I was filling up the details one by one. Later, the idea of why don't I use NLP++ to create the text that is required for the dictionaries made my work easier, where I created another analyzers for them and completed my task that I intended to.

This week's work was satisfying and easier after learning those concepts over past few weeks which made the work simpler and faster. 

WEEK 4

 WEEK 4:

Day 1: I worked on completing the output files as suggested by David and was able to generate it but, not in an efficient manner.

Day 2&3: I worked on generating the output for the exercise in an efficient manner, tried for various combinations of variables and ideas of creating the required manner.

Day 4: Clarified the doubts on the ways to debug my methods and the use of debug.txt

WEEK 3:

 WEEK 3:

 The week focused on learning the topics of concepts and functions with their usage in the PRE and POST regions. 

      

      The tweets required for the sentiment analysis were retrieved with contents like user_id, date_of_tweets and the user tweets. The tweets need to be more generic for a specific team and the analysis to be performed on it.

        Focusing on the pre-processing of the tweets for further stages in the project.

       

        

WEEK 2

WEEK 2:

 

·        The week started on the exercise given by David to create an NLP++ analyzer called ParseAddresses, which has been completed with few doubts to be discussed yet.

·        Continued by studying with converting tweets online to txt files for the recent tweets to be used for the ongoing projects(which needs to be worked on). 

        Along with the work on exercise, focus was given on ongoing project for finding the dataset.

         Targeting on completing the work in finding the appropriate tweets for the project.