Create ML Model in Python for sentiment categorization
Budget: $30 – $250 USD
I am looking for an experienced freelancer to create a Machine Learning (ML) model in Python that can be used to accurately categorize text sentiment. Naive Bayes and n-grams algorithm are the algorithms needed for this project.
The dataset for this project is provided. The plan is to evaluate about 13,000 utterances from 1433 dialogues from a TV series and characterize their multiparty dialogues into sentiments. The collected dialogues were categorized according to their dialogue length, i.e. the number of utterances in a dialogue, into four classes of which bucket length ranges are [5, 9], [10, 14], [15, 19], and [20, 24]. Finally, they randomly sampled 250 dialogues from each class to construct a dataset containing 1,000 dialogues. The data is labels with six basic emotions but I need these simplified down to three. I can provide more details as needed.
I also needed the model evaluated to measure precision, recall and F1 score.
If you are confident in your Python ML skills and have the expertise to create this model on a tight timeline, please apply!
The dataset for this project is provided. The plan is to evaluate about 13,000 utterances from 1433 dialogues from a TV series and characterize their multiparty dialogues into sentiments. The collected dialogues were categorized according to their dialogue length, i.e. the number of utterances in a dialogue, into four classes of which bucket length ranges are [5, 9], [10, 14], [15, 19], and [20, 24]. Finally, they randomly sampled 250 dialogues from each class to construct a dataset containing 1,000 dialogues. The data is labels with six basic emotions but I need these simplified down to three. I can provide more details as needed.
I also needed the model evaluated to measure precision, recall and F1 score.
If you are confident in your Python ML skills and have the expertise to create this model on a tight timeline, please apply!