While sentiment analysis has become a hot topic these days, accurate results are still something elusive to many.
Common methods include using a polarity dictionary to assign a sentiment value to individual words, such as whether a word represents a positive or negative sentiment or opinion, and machine-learning methods using human labeled training data. However, each method has its own limitations, which can often severely hinder the accuracy of results.
It is a known fact that sentiment analysis cannot handle things like sarcasm, or anything that requires deep social or cultural knowledge. In addition to that, one of the major limitations of commonly-used sentiment analysis methods is the inability to understand context. Below are a few illustrative sentences:
In these simple sentences, even though the sentence structures and words are very similar, whether the sentiment value of a sentence is positive or negative largely depends on the context.
Commonly used methods are generally incapable of discerning such contextual differences, and will likely produce very biased or even random results.
Based on our in-depth research in how linguistic expressions carry and convey information, as well as how they convey opinions or sentiments, we've developed unique natural language technologies that can accurately handle contextual information in determining the true sentiment or opinion value of a linguistic expression, and achieve a much higher accuracy rate than conventional approaches.
Try a few examples of your own, and also compare our results with other sentiment analysis providers such as
and see how much better our system can perform, and how much real value we can deliver.
We'd like to hear from you, as our algorithms are constantly being refined to produce better results. We also provide an API for integrating our superior results and technologies into your products and services. Contact us at info@LinfoResearch.com.