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.
- Clone this project
- 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 |
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Run the main.py file located at the src subfolder
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Give Lexi your dataset's name. It should be located in the same directory where you cloned Lexi to.
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Give Lexi the threshold count on which word is considered relevant (= how many times it needs to appear to be relevant).
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Give Lexi the positive rating threshold (= rating that is larger than the given value is considered positive).
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Give Lexi the negative rating threshold (= rating that is smaller than the given value is considered negative).
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Tell Lexi to use, or not to use negation detection (using negation detection usually boosts the accuracy a little bit).
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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.
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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.
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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 |
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}
}