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> (2019) Sentiment analysis for text with Deep, Learning
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VAL
Дата 5.04.2019 17:19
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(2019) Sentiment analysis for text with Deep Learning
Источник: https://towardsdatascience.com/sentiment-an...ng-2f0a0c6472b5

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I started working on a NLP related project with twitter data and one of the project goals included sentiment classification for each tweet. However when I explored the available resources such as NLTK sentiment classifier and other resource available in python, I was disappointed by the performance of these models. At most I would get about 60% to 70% accuracy on binary classification (i.e only positive or negative class) tasks.

Hence I started researching about ways to increase my model performance. One of the obvious choices was to build a deep learning based sentiment classification model.


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There are 5 major steps involved in the building a deep learning model for sentiment classification:

Step1: Get data.

Step 2: Generate embeddings

Step 3: Model architecture

Step 4: Model Parameters

Step 5: Train and test the model

Step 6: Run the model


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VAL
Дата 5.04.2019 17:19
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Мэтр, проФАН любви... proFAN of love
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QUOTE
Step1: Get data

Sourcing the labelled data for training a deep learning model is one of the most difficult parts of building a model. Fortunately we can use the Stanford sentiment treebank data for our purpose.

The data set “dictionary.txt” consists of 239,233 lines of sentences with an index for each line. The index is used to match each of the sentences to a sentiment score in the file “labels.txt”. The score ranges from 0 to 1, 0 being very negative and 1 being very positive.


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