Update README.md

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README.md

@@ -11,11 +11,14 @@ license: mit
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 # Politweet
-In this summer project at Softhouse, we have developed a tool for analyzing Twitter posts by Swedish party leaders. The UI of this tool is in the form of a webpage which lets a user see a graphical representation of the predicted features: topics, sentiments (positive, negative or neutral), and targets in the party leaders' tweets. 
+In this summer project at Softhouse, we have developed a tool for analyzing Twitter posts by Swedish party leaders. 
+The UI of this tool is in the form of a webpage which lets a user see a graphical representation of the predicted 
+features: topics, sentiments (positive, negative or neutral), and targets in the party leaders' tweets. 
 
 ### Data Gathering
-The tweets were gathered using the Twitter scraping tool [Twint](https://github.com/twintproject/twint).
-"An advanced Twitter scraping & OSINT tool written in Python that doesn't use Twitter's API, allowing you to scrape a user's followers, following, Tweets and more while evading most API limitations.".
+The tweets were gathered using the Twitter scraping tool [Twint](https://github.com/twintproject/twint).  
+"An advanced Twitter scraping & OSINT tool written in Python that doesn't use Twitter's API, allowing you to 
+scrape a user's followers, following, Tweets and more while evading most API limitations.".
 
 ### Predicting: Topics, Sentiments & Targets
 The classifications that are given by GPT-3 for every tweet contain:
@@ -24,24 +27,45 @@ The classifications that are given by GPT-3 for every tweet contain:
 - ```sentiment``` - is the tweet positive, negative or neutral?
 - ```target``` - who is the tweet targeting?
 
-The predicted features were extracted using AI, and more specifically by utilizing the autoregressive language model GPT-3 by [OpenAI](https://openai.com/api/) in combination with our prompt engineering. The final prompt is a result of experimentation while also trying to limit the length of the prompt. Since OpenAI charge users based on "tokens", which is closely related to number of words, it would be economically unsuitable to use lengthy prompts when classifying several thousands of tweets.
+The predicted features were extracted using AI, and more specifically by utilizing the autoregressive 
+language model GPT-3 by [OpenAI](https://openai.com/api/) in combination with our prompt engineering. 
+The final prompt is a result of experimentation while also trying to limit the length of the prompt. 
+Since OpenAI charge users based on "tokens", which is closely related to number of words, it would be 
+economically unsuitable to use lengthy prompts when classifying several thousands of tweets.
 
 ### Merging Topics & Targets
-Since the output from GPT-3 varies a lot, e.g. topics can be similar but not identical, a method for clustering similar topics and targets was needed in order to be able to represent statistics of these classifications. Thus, the NN-algorithm was implemented using the cosine similarity as metric, after transforming topics and targets to dense vector representaitons with [Sentence Transformers](https://github.com/UKPLab/sentence-transformers). The similarities between the classification from GPT-3 and words from a predefined set of classes are then calculated, and the classification is changed to the predefined class that yielded the highest cosine similarity. It is worth noting that each predefined class has several "synonyms" or categorical words, and that the highest cosine similarity can be found between the classification and a word from that list. 
-
-Example - The GPT-3 classified topics are: ```main_topic = "sport"``` and ```sub_topic = "soccer"``` -> ```old_topic = "sport and soccer"```, and gets the highest similarity when compared to the word/synonym "soccer" which is in the subset to the predefined class ```"Civil society and sport"``` -> ```new_topic = "Civil society and sport"```
+Since the output from GPT-3 varies a lot, e.g. topics can be similar but not identical, a method for 
+clustering similar topics and targets was needed in order to be able to represent statistics of these 
+classifications. Thus, the NN-algorithm was implemented using the cosine similarity as metric, after 
+transforming topics and targets to dense vector representations with 
+[Sentence Transformers](https://github.com/UKPLab/sentence-transformers). The similarities between the 
+classification from GPT-3 and words from a predefined set of classes are then calculated, and the 
+classification is changed to the predefined class that yielded the highest cosine similarity. It is worth 
+noting that each predefined class has several "synonyms" or categorical words, and that the highest cosine 
+similarity can be found between the classification and a word from that list. 
+
+Example - The GPT-3 classified topics are: ```main_topic = "sport"``` and ```sub_topic = "soccer"``` -> 
+```old_topic = "sport and soccer"```, and gets the highest similarity when compared to the word/synonym 
+"soccer" which is in the subset to the predefined class ```"Civil society and sport"``` -> 
+```new_topic = "Civil society and sport"```
 
 
 ### Website User Manual 
-1. Enter the time period of the tweets that you want to look into - The dates need to be on the format "YYYY-MM-DD", and between 2021-06-28 and 2022-08-10. It is preferable to choose a bigger span rather than a smaller one. Keep in mind that tweets the number of tweets posted by the party leaders will vary a lot (Annie Lööf: 1814 vs Jimmie Åkesson: 185 tweets in total). 
+1. Enter the time period of the tweets that you want to look into - The dates need to be on the format 
+"YYYY-MM-DD", and between 2021-06-28 and 2022-08-10. It is preferable to choose a bigger span rather than a 
+smaller one. Keep in mind that tweets the number of tweets posted by the party leaders will vary a lot 
+(Annie Lööf: 1814 vs Jimmie Åkesson: 185 tweets in total). 
 2. Select the party leader(s) you want to look into - At least one party leader has to be selected.
 3. Select the classifications you want to see statistics of: topic, sentiment and/or target.
 4. Apply - always press this button after you check a new box or change the dates to update the website.
 5. Run
-The piecharts and bar graphs should appear for your selected party leaders. Under the plots, a new panel will appear which lets users see how a prediction was made, i.e. classification from GPT-3 -> the   
-6. To see examples of how the topic/sentiment/target was predicted, the user can select a type of classification and check the box "show stats". To download the CSV file containing all tweets and classifications for the checked party leaders, the user can check "Export file". After the selections, Apply and Run.
+The pie charts and bar graphs should appear for your selected party leaders. Under the plots, a new panel 
+will appear which lets users see how a prediction was made, i.e. classification from GPT-3 -> the   
+6. To see examples of how the topic/sentiment/target was predicted, the user can select a type of 
+classification and check the box "show stats". To download the CSV file containing all tweets and 
+classifications for the checked party leaders, the user can check "Export file". After the selections, Apply and Run.
 
-### Dataframe Structure
+### Data Frame Structure
 Each row in the database has the following structure:
 ```id,tweet,date,user_id,username,urls,nlikes,nreplies,nretweets,class_tuple,main_topic,sub_topic,sentiment,target,merged_tuple,merged_topic,merged_target,cos_sim_topic,synonym_topic,cos_sim_target,synonym_target```.