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1 | | -# Exploration of Sentiment Analysis using Lexicon and Machine-Learning Based Methods |
| 1 | +## Exploration of Sentiment Analysis using Lexicon and Machine-Learning Based Methods |
2 | 2 |
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3 | 3 | This repo provides the submission entry for an in-class NLP sentiment analysis competition held at Microsoft AI Singapore group using techniques learned in class to classify text in identifying positive or negative sentiment. |
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5 | 5 | Data for this in-class competition comes from the Sentiment140 dataset where the training and test data consists of randomly sampled 10% and 5% of the Sentiment140 dataset. |
6 | 6 |
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7 | | -Alternatively, you can view the codes in Google Colab [here](https://drive.google.com/open?id=1d_po5AQDFRovk4livi2kvv1hhjPLxqAC). |
| 7 | +- Text Pre-processing |
| 8 | +- VADER (a VALENCE based sentiment analyzer) |
| 9 | +- Naive Bayes |
| 10 | +- Linear SVM (Support Vector Machine) |
| 11 | +- Decision Tree |
| 12 | +- Random Forest |
| 13 | +- Extra Trees |
| 14 | +- SVC |
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9 | 16 |  |
10 | 17 |
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| 18 | +## Getting started |
| 19 | +Open `SentimentAnalysis.ipynb` on a jupyter notebook environment. Alternatively, you can view the codes in Google Colab [here](https://drive.google.com/open?id=1d_po5AQDFRovk4livi2kvv1hhjPLxqAC). The notebook consists of further technical details. |
11 | 20 |
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| 21 | +# Improvements |
| 22 | +Could potentially explore the use of Deep Learning Techniques such as RNN and/or LSTM for sentiment analysis |
| 23 | + |
| 24 | +<!--- |
12 | 25 | # Walk-through of the submission entry: |
13 | 26 | |
14 | 27 | |
@@ -1013,6 +1026,6 @@ preprocess.head() |
1013 | 1026 | </table> |
1014 | 1027 | </div> |
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