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An aspect-based sentiment analysis on dating apps reviews

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What do you like in dating apps

In the last years, dating apps have become more and more popular, and have caught the attention of researchers studying the interplay between new media technologies and society.

Studies have analyzed the motives driving people to use these apps, finding them ranging from casual sex to simply killing time, as well as involving romantic pursuits and other kinds of affiliation and information. Moreover, gender differences in app uses seem to be prominent, with men primarily pursuing hook-up sex, travelling and relationships, and women more prone to seek friendship and self-validation.

The aim of this project is to exploit the descriptive power of aspect-based sentiment analysis to try to automatically detect a polarity (and possibly an opinion) for these social impacting aspects. A complete description of the project is available in this report.

Models

Three different models were conceived for this purpose:

The models were evaluated on two datasets containing reviews about Tinder, Bumble and Hinge.

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Running

To run all the experiments, type the following commands:

virtualenv venv 
source venv/bin/activate
pip install -U pip setuptools wheel
pip install -U spacy
python -m spacy download en_core_web_sm
pip install -r requirements.txt

You also need to download the two datasets from here and here and place them in a data/ folder. Before executing code in syntax_lexicon.ipynb, run:

python3 preprocess_spacy.py <app [tinder, bumble, hinge]>

This will create a set of spacy-preprocessed reviews for part-of-speech tagging, stored in a data/<app>_spacy folder.