Actuate and Bitext Announce Collaboration to Deliver Text Analytics Engines and Sentiment Analysis for Big Data through BIRT

Actuate Corporation, The BIRT Company^™ delivering more insights to more people than all BI companies combined, today announced their cooperation with Bitext, in parallel with Bitext’s U.S. event in San Francisco this evening at WeWork. Bitext provides text analytics engines – inherently multilingual semantic technologies including text analytics and natural language interfaces – sporting one of the highest degrees of accuracy available today. Bitext recently announced a partnership with Salesforce.com as well.

Combined with Actuate’s BIRT iHub™ development tools and platform, or with Actuate’s BIRT Analytics™ 4.2 predictive analysis solution, Bitext provides two main advantages for the BIRT developer or end user: it produces highly accurate precision and recall; and it lends itself easily to a development process based on continuous improvement. BIRT Analytics 4.2 and Bitext are now available as a combined solution from Actuate.

“We are very pleased to be working together with Actuate to further enrich their leading BIRT commercial suite with Bitext text and semantic analysis power,” said Antonio Valderrabanos, CEO and Founder, Bitext. “With Bitext analyzing unstructured data words as well as meaning, and Actuate performing advanced analysis of structured as well as unstructured, we cover the world of data.”

Bitext enables entity and concept extraction, categorization, and sentiment analysis with a focus on customer-centric business areas such as marketing; customer relationship management and support; content analytics; and any line of business unit that requires advanced analytics. Examples of solutions include text analytics (entity extraction, concept extraction, and sentiment analysis), metatagging (enhanced indexing) and search (natural language interfaces). Currently available for 10 languages, Bitext enables the addition of new languages by including new data sources (dictionaries and grammatical rules).

“Our collaboration with Bitext – providers of advanced semantic solutions for social media, search, and more – extends the types of analysis that can be performed with Actuate’s commercial BIRT developer and end-user platform or solution, by adding the ability to score sentiment toward products and services,” said Josep Arroyo, VP of Analytic Solutions at Actuate. “Users of Actuate with Bitext can now tap more than just negative or positive sentiment analysis. They can also visualize anticipated risks, opportunities and threats for personalized insights, in a single display on any device.”

For a demo of BIRT Analytics 4.2, please visit Actuate’s YouTube Channel

For a demo of Bitext’s new API, please visit Bitext website

Read the full Press Release.

Bristol Social Media analytics company track Scottish Independence

As we know, social media monitoring or buzz monitoring is used in many sectors such as marketing, after-sales services or opinion polls. In 2010, Tweetminster has realized an analysis on buzz generated on Twitter about the British national elections. With a margin of error of 1.75%, the tool has shown a more accurate results than wellknown companies like YouGov145. This event had launched a debate on the reliability of measured data through social medias and on the possible correlation between the buzz generated and people's opinions.

Today, Bristol Social Media analytics company track Scottish Independence. What are people saying about Scottish Independence on social media? This has been an issue since the Act of Union was signed in 1707 – or even before that, going back to the Declaration of Arbroath in 1320 or the Battle of Bannockburn in 1314. Now the Scottish National Party Government in Edinburgh is planning to hold a referendum on Independence in 2014.

But is this relevant to people in Bristol? Churnbar, a Bristol-based social media monitoring and text analytics company, have produced a report on seven weeks of conversation in social media and online news, which shows that the issues raised by Scottish Independence could affect people south of the border.

More information about Bristol Social Media analytics company

2 questions about Language technology challenges

I have two questions about sentiment analysis: 

First, how to assign the correct sentiment to a comment when "dictionaries used for automatic analysis of sentiments should be able to take account of: polarity of feeling and the degree of positivity, detection of subjectivity and identification of an opinion, the point of view,  the non-factual informations (related to the six universal emotions: anger, disgust, fear, joy, sadness and surprise), syntax and negation"?

Second, how to sort and value the comments that "deserve more interest than others, and [that] can be crucial in the establishment of a new strategy"?

For now, the answer to both questions is: human analysis. Although they have their own interpretation, they apprehend posts subjectively and are slower, human analysts remain today the most efficient in awarding the correct tone to a comment. Tomorrow, technologies as used by Watson, the IBM supercomputer, will able to achieve a degree of precision so that they will become ready to take over  the human analysis.

Source : Master Thesis "Mobility sector in Belgium : social media monitoring to built an efficient marketing strategy" 2011, ULB.

Language = Intelligence

Challenges of sentiment analysis tools in Buzz monitoring sector

(...) If topics can be automatically classified in a relatively simple way, sentiment analysis (positive, negative or neutral) gets harder as it requires a global analysis of the monitored text. This may be particularly difficult in cases of coordination between different parts of a sentence, of an anaphora or of a coreference (the recovery of an argument later in this document). 

Another difficulty of natural language for automatic analysis of sentiments are intentional contexts, for which the expression of opinion is not a true feeling. Thus, the phrase" I thought the trams were more modern" may be identified as positive when the intention is, however, negative. If the analysis of conversations are established by "word packets", the sentences "I like this model not only because of ..." and "I did not like this model only because of ... "a real problem. They are composed of the same packet of words but represent an opposite feeling. Lucas Dini et al. "Showed the relationship between syntactic and semantic structures of a sentence and the expression of the opinion that vehicle". 

The algorithm must be able to differentiate a neutral comment (eg: this car announces a fuel consumption of 3.3 l/100km) from an opinion (eg: this car announces a fuel consumption of only 3.3 l/100km!). It should also detect spelling errors, simplified syntaxes, slang or smileys. Finally it may be that a comment has positive and negative elements. Are they equal in value or is it that the positives outweigh? The phrase "I think this car is the best in its segment ... but also the most expensive "is an example of such ambiguity. What did the author say ? It is normal that its price is more high because of its beauty or the contrary, the price is not worth the candle? Even a human would have difficulty assigning a tone here. (...)

Source : Master Thesis "Mobility sector in Belgium : social media monitoring to built an efficient marketing strategy" 2011, ULB.

Language = Intelligence

Good News vs Bad News : the sentiment analysis tool that can change buzz monitoring practices

Across news sites, social media platforms, blogs and forums, customers are responding to what companies are doing every minute, but do we really know what they are saying ? As professionals we have all come across buzz monitoring or sentiment analysis tools to help us uncover the weak signals from the strong ones, but how usable are they ? 

You could be a trader looking for a quick and timely overview on the company you’re about to invest in, or a researcher wanting to understand the current landscape around a brand, the requirement for a tool that is usable and time efficient will be the same in most cases. 

That’s where Good News Vs. Bad News can help. "GoodNews Vs BadNews" is a free browser-based app that enables marketers, publishers and businesspeople alike to discover the positive and negative sentiments around their brand. The technology powering the application is comprised of a staggering 8 million lines of code and is used across the world by companies ranging from Microsoft to Motorola.

Source : LangTechNews