Lexalytics Unveils Sentiment Analysis of Emoticons, Acronyms
First OEM Engine to Examine Short Form Content for Sentiment Analysis Boston, MA—September 20, 2010Lexalytics, Inc. (http://www.lexalytics.com), a software and services company specializing in text and sentiment analysis, announced the availability of richer reporting on the conversations occurring around, about, and between different accounts on Twitter based on the sentiment analysis of commonly used emoticons […]
First OEM Engine to Examine Short Form Content for Sentiment Analysis
Boston, MA—September 20, 2010
Lexalytics, Inc. (http://www.lexalytics.com), a software and services company specializing in text and sentiment analysis, announced the availability of richer reporting on the conversations occurring around, about, and between different accounts on Twitter based on the sentiment analysis of commonly used emoticons and acronyms.
With the use of emoticons, abbreviations, and confusing “social speak” grammar, micro-blog services such as Twitter present a difficult task for natural language processing systems. These improvements come as part of the yearly software license for Salience, Lexalytics’ core text analytics engine.
“We spent a few months enabling our software to better deal with such content,” said Lexalytics CEO Jeff Catlin. “The improvements we made make processing this content significantly more valuable.”
For acronyms, the Lexalytics team parsed thousands of tweets to get to hundreds of common acronyms and emoticons. The team then made decisions on whether each acronym was sentiment-bearing, needed to be expanded, or should be treated as simply an interjection. For example:
- LOL (Laugh Out Loud)–Does not carry sentiment, nor does expanding it add any value to the resulting lexical processing; treated as an interjection
- FTW: (For The Win)–Carries positive sentiment
- IDK: (I Don’t Know) –Is useful when expanded out to its individual words
With emoticons, some are obviously positive or negative while others are considered more neutral.
For the @ sign, Salience part-of-speech tags the @ tagged string as a “MENTION” which can be used for further reporting. In particular, @ tagged strings will return as people entities, with the associated sentiment, themes, etc.
Additionally, # sign (hashtags) are part-of-speech tagged as @hashtag. These do not report back as any sort of entity type. Hashtags are typically used as a lightweight “tag” for the content of the tweet.
his information can be used by Salience for further processing as a tag.
Check out the new capabilities at http://www.lexalytics.com/demo. Select the “Twitter” radio button. Paste a tweet in and test out what Lexalytics returns.
Lexalytics’ out-of-the-box, business critical text analytics and sentiment solutions allows companies to monitor and react in real-time by making sense of the vast repositories of information from sources as diverse as Twitter, blogs, RSS feeds, web sites and in-house content. Lexalytics solutions include entity extraction, theme discovery, and sentiment analysis at the entity-level.
Processing billions of unstructured documents every day globally, Lexalytics is the industry leader in translating text into profitable decisions. Lexalytics deploys state-of –the-art cloud and on-prem text and sentiment analysis technologies that transform customers’ thoughts and conversations into actionable insights. The on-premise Salience® and SaaS Semantria® platforms are implemented in a variety of industries for social media monitoring, reputation management and voice of the customer programs. Based in Boston, MA, Lexalytics has offices in the US and Canada. For more information, please visit www.lexalytics.com, email firstname.lastname@example.org or call 1-617-249-1049. Follow Lexalytics on Twitter, Facebook, and LinkedIn.