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Benchmarking Sentiment Analysis Algorithms

Sentiment Analysis, also known as opinion mining, is a powerful tool you can use to build smarter products. It’s a natural language processing algorithm that gives you a general idea about the positive, neutral, and negative sentiment of texts. Sentiment analysis is often used to understand the opinion or attitude in tweets, status updates, movie/music/television reviews, chats, emails, comments, and more. Social media monitoring apps and companies all rely on sentiment analysis and machine learning to assist them in gaining insights about mentions, brands, and products.

Want to learn more about Sentiment Analysis? Read the Algorithmia Guide to Sentiment Analysis.

For example, sentiment analysis can be used to better understand how your customers perceive your brand or product on Twitter in real-time. Instead of going through hundreds, or maybe thousands, of tweets by hand, you can easily group positive, neutral, and negative tweets together, and sort them based on their confidence values. This will save you countless hours and leads to actionable insights.

Sentiment Analysis Algorithms On Algorithmia

Anyone who has worked with sentiment analysis can tell you it’s a very hard problem. It’s not hard to find trained models for classifying very specific domains, but there currently isn’t an all-purpose sentiment analyzer in the wild. In general, sentiment analysis becomes even harder when the volume of text increases, due to the complex relations between words and phrases.

On the Algorithmia marketplace, the most popular sentiment analysis algorithm is nlp/SentimentAnalysis. It’s an algorithm based on the popular and well known NLP library called Stanford CoreNLP, and it does a good job of analyzing large bodies of text. However, we’ve observed that the algorithm tends to return overly negative sentiment on short bodies of text, and decided that it needed some improvement.

We’ve found at that the Stanford CoreNLP library was originally intended for building NLP pipelines in a fast and simple fashion, and didn’t focus too much on individual features, and lacks documentation about how to retrain the model. Additionally, the library doesn’t provide confidence values, but rather returns an integer between 0 and 4.

A Better Alternative

We decided to test out a few alternative open-source libraries out there that would hopefully outperform the current algorithm when it came to social media sentiment analysis. We came across an interesting and quite well performing library called Vader Sentiment Analysis. The library is based on a paper published by SocialAI at Georgia Tech. This new algorithm performed exceptionally well, and has been added it to the marketplace. It’s called nlp/SocialSentimentAnalysis, and is designed to analyze social media texts.

We ran benchmarks on both nlp/SentimentAnalysis and nlp/SocialSentimentAnalysis to compare them to each other. We used an open dataset from the Crowdflowers Data for Everyone initiative. The Apple Computers Twitter sentiment dataset was selected because the tweets covered a wide array of topics. Real life data is almost never homogeneous, since it almost always has noise in it. Using this dataset helped us better understand how the algorithm performed when faced with real data.

We removed tweets that had no sentiment, and then filtered out anything that didn’t return 100% confidence so that the tweets were grouped and labeled tweets by consensus. This decreased the size of the dataset from ~4000 tweets to ~1800 tweets.

Running Time Comparison

The first comparison we made was the running time for each algorithm. The new nlp/SocialSentimentAnalysis algorithm runs up to 12 times faster than the nlp/SentimentAnalysis algorithm.

fig01

Accuracy Comparison

As you can see in the bar chart below, the new social sentiment analysis algorithm performs 15% better in overall accuracy.

fig02

We can also see how well it performs in accurately predicting each specific label. We used the One-Versus-All method to calculate the accuracy for every individual label. The new algorithm outperformed the old one in every given label.

fig03

When To Use Social Sentiment Analysis

The new algorithm works well with social media texts, as well as texts that inherently have a similar structure and nature (i.e. status updates, chat messages, comments, etc). The algorithm can still give you sentiments for bigger texts such as reviews, or articles, but it will probably be not as accurate as it is with social media texts.

An example application would be to monitor social media for how people are reacting to a change to your product, such as when Foursquare split their app in two: Swarm and Foursquare. Or, when Apple releases an iOS update. You could monitor the overall sentiment of a certain hashtag or account mentions, and visualize a line chart that demonstrates the change of your customer’s sentiment over time.

Another example, you could monitor your products through social media 24/7, and receive alerts when significant changes in sentiment happen to your product or service in a short amount of time. This would act as an early alert system to help you take quick, and appropriate action before a problem gets even bigger. An example would be a brand like Comcast or Time Warner wanting to keep tabs on customer satisfaction through social media, and proactively respond to customers when there is a service interruption.

Understanding Social Sentiment Analysis

The new algorithm returns three individual sentiments: positive, neutral, and negative. Additionally, it returns one general overall (i.e. compound) sentiment. Each individual sentiment is scored between 0 and 1 according to their intensity. The compound sentiment of the text is given between -1 and 1, which is between absolute negative and absolute positive, respectively.

Based on your use case and application, you may want to only use a specific individual sentiment (i.e. wanting to see only negative tweets, ordered by intensity), or the compound, overall sentiment (i.e. understanding general consumer feelings and opinions). Having both types of sentiment gives you the freedom to build applications exactly as you need to.

Conclusion

The new and improved nlp/SocialSentimentAnalysis algorithm is definitely faster, and better at classifying the sentiments of social media texts. It allows you to build different kinds of applications due to it’s variety of sentiment types (individual or compound), whereas the old one only provided a overall sentiment with five discrete values, and is better reserved for larger bodies of text.

Did you build an awesome app that uses nlp/SocialSentimentAnalysis? Let us know on Twitter @algorithmia!

Bonus: Check out the source code for running the benchmarks yourself @github!

Algorithm Engineer at Algorithmia, helps make complicated things simpler. Believes that Machine Intelligence will have a huge impact on our lives in the days to come, and hopes to have a defining role in shaping this new future.

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