Lexalytics Brings Sentiment Analysis to Android Developers
Lexalytics deploys state-of-the-art cloud and on-premises text and sentiment analytics that can turn customers' conversations into actionable insights.Lexalytics, which makes text-analytics software for both cloud and on-premises systems, has launched what it claims to be the first native text-analytics package for a mobile operating system—in this case, Android. The Boston-based company has been providing semantic text-analysis processing for large corporations for 13 years; now, as of April 6, it has released the technology to the mobile app developer community. Using Lexalytics' Salience for Android, mobile application developers can integrate sophisticated natural-language processing and analytics into any app that uses text—email, SMS and chat, reviews, comparison shopping, social media, travel, hospitality and others—to gain immediate insights that can benefit the app user. Because Salience provides native text analytics, all processing stays local on the phone so that analysis results from users never go back to the cloud, ensuring data privacy.
Lexalytics deploys state-of-the-art cloud and on-premises text and sentiment analytics technologies that can turn customers' thoughts and conversations into actionable insights for the app user. The on-premises Salience and SaaS Semantria platforms are used in a variety of industries for social media monitoring, reputation management and voice of the customer programs.
- immediately alerting a user about an email or post that is especially negative and incendiary, or positive and praiseworthy;
- displaying a daily summary of emails from important contacts;
- providing a list of any to-do's throughout the day, week or month;
- summarizing the latest information in the sports world from a favorite team;
- removing politics-related content—or any content the user might want stricken from their social feed;
- highlight buzz-worthy events taking place in the upcoming weekend;
- warning users when they're about to send out a text they may regret later; and more.
- Named entity extraction: Provides a list of who or what is being discussed;
- Summarization: Summarizes the most important parts of an email, news story or other text data, so the user can scan and quickly grasp the meaning;
- Imperative sentence extraction: Immediately indicates when the user is being asked to do something; and
- Query-based categorization: Creates search queries to easily categorize content into different buckets.