Sentence examples for models for sentiment from inspiring English sources

These attributes make Deep Learning one of the most desirable models for sentiment analysis.

Trung et al. [55] proposed a fuzzy propagation modeling for opinion mining by sentiment analysis of online social networks.

In terms of opinion mining, a fuzzy propagation modeling for opinion mining by sentiment analysis of online social networks was proposed by Trung et al. A practical system, called TweetScope, has been implemented to efficiently collect and analyze all possible tweets from customers [18].

Based on the findings in the previous section, doc2vec is not a good model for predicting sentiment of authors regarding the stock market, and so we move on to RNNs.

Using logistic regression as the baseline and comparing results in Tables 1 and 6 reveals that LSTM is not an effective model for predicting sentiment in the StockTwits dataset.

For these reasons, we prefer to choose MaxEnt modeling as our machine learning algorithm for sentiment detection over other models, which are biased by having overlapping features (e.g., Naive Bayes models) or are used as uninterpretable black boxes (e.g., SVM or RNN).

Another model has been created for sentiment analysis of tweets, which was trained based on hashtags and emoticons [44].

Finally, experimental results demonstrate the effectiveness of the proposed models for the task of targeted sentiment classification among three different datasets.

With social media the model can monitor the online activities of people for sentiment analysis.

Extract positive and negative chunk lists from the segmented data to form a feature set prepared for sentiment classification using maximum entropy modeling.

Natural language models need annotations that teach the models the sentiment of a tweet, for example, or that a string of words is a question about the status of an online purchase.