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diff --git a/content/posts/2019-10-19-using-sentiment-analysis-for-clickbait-detection.md b/content/posts/2019-10-19-using-sentiment-analysis-for-clickbait-detection.md index 0f5d994..30b0fd4 100644 --- a/content/posts/2019-10-19-using-sentiment-analysis-for-clickbait-detection.md +++ b/content/posts/2019-10-19-using-sentiment-analysis-for-clickbait-detection.md | |||
| @@ -72,11 +72,11 @@ plt.show() | |||
| 72 | 2. Rule-based approach may not be the best way of doing this. By using deep learning we would be able to get better insights. | 72 | 2. Rule-based approach may not be the best way of doing this. By using deep learning we would be able to get better insights. |
| 73 | 3. **Next step would be to** periodically fetch RSS items and store them over a longer period of time and then perform analysis again and use either machine learning or deep learning on top of it. | 73 | 3. **Next step would be to** periodically fetch RSS items and store them over a longer period of time and then perform analysis again and use either machine learning or deep learning on top of it. |
| 74 | 74 | ||
| 75 |  | 75 |  |
| 76 | 76 | ||
| 77 | Figure above displays difference between title and description sentiment for specific RSS feed item. 1 means positive and -1 means negative sentiment. | 77 | Figure above displays difference between title and description sentiment for specific RSS feed item. 1 means positive and -1 means negative sentiment. |
| 78 | 78 | ||
| 79 | [» Download Jupyter Notebook](/sentiment-analysis/sentiment-analysis.ipynb) | 79 | [» Download Jupyter Notebook](/assets/sentiment-analysis/sentiment-analysis.ipynb) |
| 80 | 80 | ||
| 81 | ## Going further | 81 | ## Going further |
| 82 | 82 | ||