Sentiment Analysis

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Predict sentiment from text.

Signals

Inputs:

Outputs:

Description

Sentiment Analysis predicts sentiment for each document in a corpus. It uses Liu Hu and Vader sentiment modules from NLTK. Both of them are lexicon-based.

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  1. Method:
    • Liu Hu: lexicon-based sentiment analysis
    • Vader: lexicon- and rule-based sentiment analysis
  2. Produce a report.
  3. If Auto commit is on, sentiment-tagged corpus is communicated automatically. Alternatively press Commit.

Example

Sentiment Analysis can be used for constructing additional features with sentiment prediction from corpus. First, we load Election-2016-tweets.tab in Corpus. Then we connect Corpus to Sentiment Analysis. The widget will append 4 new features for Vader method: positive score, negative score, neutral score and compound (combined score).

We can observe new features in a Data Table, where we sorted the compound by score. Compound represents the total sentiment of a tweet, where -1 is the most negative and 1 the most positive.

../_images/Sentiment-DataTable.png

Now let us visualize the data. We have some features we are currently not interested in, so we will remove them with Select Columns.

../_images/Sentiment-SelectColumns.png

Then we will make our corpus a little smaller, so it will be easier to visualize. Pass the data to Data Sampler and retain a random 10% of the tweets.

../_images/Sentiment-DataSampler.png

Now pass the filtered corpus to Heat Map. Use Merge by k-means to merge tweets with the same polarity into one line. Then use Cluster by rows to create a clustered visualization where similar tweets are grouped together. Click on a cluster to select a group of tweets - we selected the negative cluster.

../_images/Sentiment-HeatMap.png

To observe the selected subset, pass the tweets to Corpus Viewer.

../_images/Sentiment-CorpusViewer.png
../_images/Sentiment-workflow.png

References

Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.