smm-project's Introduction

SMM-project

Social Media Mining:

Requirements for executing:- - Python 3.0 - Executable in Jupyter notebook

Python Packages: - pandas - urllib.request - json - pprint - everygrams from NLTK - word_tokenzie from NLTK - stopwords from NLTK.corpus - defaultdict from collections - joblib from sklearn.externals - numpy - preprocessing from sklearn

Models input files: - /Model/sentiment_model3.0.sav - /Model/tfidf_vectorizer3.0.sav

Tweets input files: - /Raw_data/full_LA_Oct-Dec2017.csv - /Raw_data/pointOfInterests.csv

Steps to follow: Location Entity Extraction:

Edit setup.py file. Change the location name of nltk folder as per your nltk installation. nltkFolderName="C:/Users/Bhagyashree/AppData/Roaming/nltk_data/corpora/conll2002/"
Run "python setup.py" file from LocationEntityExtraction folder.
"/LocationEntityExtraction/processedDataset/pointOfInterests.csv" - points of interests file will be created.
Copy /LocationEntityExtraction/processedDataset/pointOfInterests.csv to /TwitterDataTourism-sentimentnalysiswith1.6M/Raw_data/pointOfIntersts.csv

Get top tourist spots:

Go to this folder "TwitterDataTourism-sentimentnalysiswith1.6M"
Open this "GetTopTourstSpots.ipynb" notebook - GetTopTourstSpots.ipynb : This file describes the process of executing the GetTopTouristSpots.ipynb
Run all code inside it.
The final result will be saved to "/TwitterDataTourism-sentimentnalysiswith1.6M/Processed_data/FinalReport.csv" after the succesfull execution of the script.

To get characteristics of city:

Go to "/CharacteristicsOfCity/"
Run "python Characteristics.py"
Output is created in same folder "characteristics.csv"

To get popular hashtags:

Go to folder "/Hashtag-extraction/"
Run python notebook "ProminentHashtags.ipynb"
Output is in "/Hashtag-extraction/ProcessedDataSet/hashtag_NY.csv". Note: Current hashtag extraction is for New York dataset.

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