2 minutes to read. By author Michaela Mora on May 3, 2015 Topics: Analysis Techniques, Market Research Cartoons
The state of sentiment analysis is disappointing. After the initial enthusiasm about the concept and the flood of many tools trying to measure it, companies are searching for better ways to measure and apply sentiment analysis to business problems.
This was apparent at the 2015 Social Media & Web Analytics conference, organized by Innovation Enterprise in San Francisco. Many attendees came from big tech companies such as LinkedIn, Twitter, and Yahoo. I heard a clear consensus about how problematic and inaccurate Sentiment Analysis was when left to machines counting positive and negatives comments.
The human language is too complex to leave it solely to algorithms. Emerging terms, negation, sarcasm, word roots (stemming), sentence completeness, term frequency, semantic ambiguity, lack of context, and differences in topic categories, among others, are issues undermining the accuracy of the Sentiment metrics currently used. Consequently, we need humans to verify and correct the models we use to support machine learning.
Some users of Sentiment Analysis try to go beyond simple counts of positive, negative, and neutral comments by:
Nonetheless, we still need algorithms to analyze the large amounts of unstructured data generated by social media and customer feedback systems, among other sources.
As a result, the data scientist community is still searching for the best ones. More text mining tools (e.g. OdinAnswers, Bitext, Monkeylearn, Luminoso, etc.) are coming to the text analytics space. However, some companies have decided to develop their own proprietary tools. It seems they have yet to find one that satisfies all their needs or that is affordable.
Above all, aware of the limitations of this metric, some companies use Sentiment as one more variable, among many, in their analytics arsenal. They combined it with others in the context of the business problems they face to:
In conclusion, my hope is that the frustration with the current state of Sentiment Analysis keeps driving the development of better tools. We need text analytics tools to extract insights from unstructured data faster and in a more cost-effective way. In the hands of market researchers, these tools can facilitate and speed up the analysis of qualitative data and open-ended survey questions.
Share on:
Subscribe to our newsletter to get notified about future articles
Subscribe and don’t miss anything!
Subscribe
This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.
Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings.
If you disable this cookie, we will not be able to save your preferences. This means that every time you visit this website you will need to enable or disable cookies again.