This project performs topic and sentiment analysis on an arabic & english datasets from the hashtags #JerusalemEmbassy and #القدس_عاصمة_فلسطين_الابدية and then creates graphml files that visualize the dataset and political sides it contains. The following steps are a walk-through for the mentioned process.

This dataset is divided into 3 phases:

1. Phase 1: Dec 6 2017 to Jan 20 2018.
2. Phase 2: Jan 21 2018 to May 09 2018.
3. Phase 3: May 10 2018 to May 24 2018.

The English dataset can be found in dataset/phase1 , dataset/phase2 and dataset/phase3. Each phase contains 3 csv files: 1 for replies, and 1 for the tweets that are replied to, and a third for non-reply tweets. The following is the sequence to follow for recreating the project for the English dataset:

1. topicSentimentEnglish.py creates an LDA model and assigns each tweet a topic and adds a sentiment column. The resulting dataframes can be found in the folder topic_sentiment_pickles .
2. topic_sentiment_dfs.ipynb is a notebook that separates all tweets that belong to the same topic with the same sentiment in a dataframe (e.g: the dataframe neg3_phase1 contains all the tweets from topic 3 with a negative sentiment in phase 1). The resulting dataframes can be found in the folder topic_sentiment_dfs.
3. wordCloudGeneratorEnglish.py contains the code to generate wordclouds from the dataframes in topic_sentiment_dfs folder. The resulting wordclouds can be found in the folder wordclouds
4. Creating the graph files: to create the graphs for each phase the following files should be run in the order specified below (Note that phaseX means phase1 or phase2 or phase3):

   1. phaseX_repliedto_graph.py

   2. phaseX_replies_graph.py

   3. phaseX_tweets_graph.py

   4. phaseX_full_graph_POL.py (This groups nodes according to political pole)

      OR

   5. phaseX_full_graph_TS.py (This groups nodes according to topic + sentiment)

The resulting graph files can be found in full_graphs_POL and full_graphs_TS folders depending on the selected step v

5. To label boundary nodes and edges and get the boundary score, the following files should be run in the order specified below:

   1. phaseX_labeling_boundary_nodes.py
   2. phaseX_labeling_boundary_edges.py
   3. phaseX_boundary_score_calculation.py

   The resulting files can be found in boundary_labeled_graphs

This dataset covers tweets from the phase defined above as phase 3. The Arabic dataset can be found in dataset/arabic The following is the sequence to follow for recreating the project for the Arabic dataset:

1. arabic_topic_extraction.ipynb performs manual topic mapping through looking for keywords in tweets. The resulting grouping can be found in arabic_topic_dfs folder
2. Creating the graph files: to create the graphs for the Arabic dataset the following files should be run in the order specified below
   1. arabic_repliedto_graph.py
   2. arabic_replies_graph.py
   3. arabic_tweets_graph.py
   4. arabic_full_graph.py

The resulting graphs can be found in the full_graph_arabic folder

3. To label boundary nodes and edges and get the boundary score, the following files should be run in the order specified below:

   1. arabic_labeling_boundary_nodes.py
   2. arabic_labeling_boundary_edges.py
   3. arabic_boundary_score_calculation.py

   The resulting files can be found in boundary_labeled_graphs