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Sentiment classifier and polarity lexicon creator

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Lexi

What is Lexi and why use it:

Lexi is first and foremost a polarity lexicon creator and it can give a prediction based on a created lexicon. The actual polarity lexicon creation process is based on a semantic orientation score, which is a well-known method to calculate polarities for words. It's not deep learning, but it works.

Installation:

  1. Clone this project

How to use Lexi:

  1. Your dataset has to be in CSV format with atleast two columns. The first two columns need to have the following structure:
text_data rating_data
String Integer or Float

For example:

text_data rating_data
This is an example 3
This also is an example 7
  1. Run the main.py file located at the src subfolder

  2. Give Lexi your dataset's name. It should be located in the same directory where you cloned Lexi to.

  3. Give Lexi the threshold count on which word is considered relevant (= how many times it needs to appear to be relevant).

  4. Give Lexi the positive rating threshold (= rating that is larger than the given value is considered positive).

  5. Give Lexi the negative rating threshold (= rating that is smaller than the given value is considered negative).

  6. Tell Lexi to use, or not to use negation detection (using negation detection usually boosts the accuracy a little bit).

  7. Tell Lexi to classify, or not to classify documents based on your generated lexicon. If you decide to classify documents, Lexi will prompt you to give a to be classified file's name and it also expects it to be found in the same directory to which you cloned Lexi to. After the classification process, Lexi's going to ask a filename for the classification results.

  8. Tell Lexi to save, or not to save the created lexicon. If you decide to save the lexicon, Lexi's going to prompt you for a filename.

  9. If everything went fine, and you decided to save your lexicon and/or classification results, the relevant files should be located in the directory where you clone Lexi to.

Example lexicon from the previous "data":

word score
this -0.3219280948873623
example -0.3219280948873623
also 0.6780719051126377
an -0.3219280948873623
is -0.3219280948873623

Example classification results from the previous "data":

data sentiment
This is an example Negative
This also is an example Positive

How to cite:

If you wish to use the data/code in your research, please cite the following:

@inproceedings{vankka2019sentiment,
  title={Sentiment Analysis of Finnish Customer Reviews},
  author={Vankka, Jouko and Myllykoski, Heikki and Peltonen, Tuomas and Riippa, Ken},
  booktitle={2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS)},
  pages={344--350},
  year={2019},
  organization={IEEE}
}