There’s more than one side to every story with Trooclick

I test applications using a natural language interface or a natural language technology and I write an article about my experience with it.

This week, I tested Trooclick, a free Opinion-Driven Search Engine.

A New Approach of Opinion Mining with Trooclick’s Opinion-Driven Search Engine

An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.

There are listening platforms identifying a list of articles talking about a brand, a business or a personality and offering opinion analysis tools: SenticNet, Luminoso and Attensity.

The problem with these solutions is that they apply a very wide crawl reporting data without much interest. The completeness of these platforms can be brought into question. Although the approach certainly is of interest in “quantifying” data, we cannot get something out of it in the “qualified” perspective. First, the result is often a hodgepodge of heterogeneous texts, often highly redundant, with little real opinions expressed (hence the category “neutral” as many opinions analysis systems put next to “positive” and “negative”) category has little value in most cases. Finally, eReputation platforms offering a function of “sentiment analysis” do this for the entire document, which quickly becomes unusable when several points of view are expressed in the same document .

Trooclick’s Opinion-Driven Search Engine (beta) uses natural language processing (NLP) technology to gather quotes (Screenshot 1) from online sources of news and opinions – including content from publishers, blogs, and Twitter (soon expanding to radio and TV). After quotes are extracted, the speakers are categorized (categories include executives, analysts, politicians, clients – 16 total).  They then use this data to rank news articles for quality. Unsurprisingly, for a company that emphasizes the importance of different points of view to understand the news, in the Trooclick universe more points of view from more people means a higher ranking. For now they have two ranking criteria active (number of speakers and quote score) with about 30 more in the pipes (Screenshot 2).

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The software, based on advanced text mining and semantic analysis technologies, performs several tasks:

  • Quality ranking of articles per event
  • extracting quotes and identifying the speaker, the media, the date of the publication
  • classification of the speaker into categories (manager, analyst, customer, employee, etc …)

Still in beta, the Trooclick’s solution is currently only available in the English-speaking media.

Automatic Detection of quotes

Trooclick tracks quotes from news sites and social media. The system identifies quotes in different ways. As a result not only does the site pull up direct quotes like this:

McDonalds will stop serving antibiotic-raised poultry,said McDonalds President, Mike Andres.

…But also indirect ones like this:

Mike Andres, McDonalds President, said McDonalds will stop serving antibiotic-raised poultry.

And some quotes come from opinion columns (“Hopefully chicken is just the start – I hope the Big Mac and McRib will be next”) and analysis (“McDonald’s decision to sell milk produced without rBST was a good step because the growth hormone can cause health problems in dairycows.”)

Automation of Fact-Checking for Journalism

Fact checking is the task of assessing the truthfulness of claims made by public figures such as politicians, pundits, etc. It is commonly performed by journalists employed by news organisations in the process of news article creation. Fact-checking is a time-consuming process » (Vlachos & Riedel 2014).

Journalism is about finding facts, interpreting their importance, and then sharing that information with the audience. That’s all journalists do: find, verify, enrich and then disseminate information. It sounds easy, doesn’t it, observing what is going on, asking questions, uncovering facts and then telling the public what we have discovered. But we are dealing with volatile raw material. Handled carelessly, the facts we uncover, research and present have the power to cause misunderstandings, damage and could, potentially, change the course of history. That’s why it’s essential that we apply robust fact-checking to all our journalism.

But, fact-checking is a time-consuming process. Automating the process of fact checking has recently been discussed in the context of computational journalism (Cohen et al., 2011Flew et al., 2012). Inspired by the recent progress in natural language processing, databases and information retrieval, the vision is to provide journalists with tools that would allow them to perform this task automatically,

One way to solve that problem is by presenting different points of view as Trooclick’s Opinion-Driven Search Engine. There are other solutions in the area of fact-checking such as Truth Teller: this Washington Post initiative transcribes political videos and checks them against a database that draws on PolitiFact,, and the paper’s own Fact Checker blog. The program then tells viewers which statements are true and which are false. In 2015, the Post plans to annotate videos in real time.

There are also approaches based on crowdsourcing as Fiskkit and Grasswire, a platforms that invites the public to fact-check breaking news stories.

Trooclick’s Opinion-Driven Search Engine is available online on

Here a video presentation of Trooclick:

To contact Trooclick

earth Web Site        twitter @Trooclick      mail Email

What do you think about Trooclick’s solution ? Please, don’t hesitate to drop your comments below.

Discover, Read, Share & Collect quickly with Reverb

Every week, I test an application using a natural language interface or natural language technology and I write an article about my experience with this application.

ReverbLogoLast week, I tested Reverb, a free news aggregator for iPad. How Reverb meets challenges of news aggregation?

News Recommander Systems

Before to answer at this question, it is necessary to present briefly the news recommendation problem.

The amount of available data on internet has considerably increased and has led to the problem of information overload. Recommender systems try to tackle this issue by offering personalized suggestions. News recommendation is an application of such systems. “The online reading practice leads to the so-called post-click news recommendation problem: when a user has clicked on a news link and is reading an article, he or she is likely to be interested in other related articles. This is still a typical editor’s task, namely an expert who manually looks for relevant content and builds a recommendation set of links, which will be displayed below or next to the current article. News recommender systems attempt to automate such task. Current strategies can be clustered into 3 main categories, namely (a) collaborative filtering focuses on the similarities between users of a service, thus relying on user pro-files data, (b) content-based recommendation that leverages term-driven information retrieval techniques to compute similarities between items, and (c) knowledge-based recommendation mines external data to enrich item descriptions” [Fossati, Giuliano & Tummarello 2012].

I think the application Reverb uses the strategie (b).

Reverb is personnalized

During the first experience, I added my interests on a Word Wall (screenshot 1) like “Natural Language Processing”, “Semantics, Linguistics”, “Machine Learning” simply by writing them (screenshot 2). Then,  Reverb created a tailored feed based on my personal interests.

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The Word Wall, a technology developped by Reverb, adapts itself according my use. For example, if I read more articles about Machine Learning, this interest comes on the beginning of the Word Wall.

Reverb is simple

With just a tap on an interest, I can read related articles (screenshot 3). By interest, the  articles are organized by chronology and by thematic. For example, in the interest “Natural Language Processing”, Reverb proposes some related interests like “Information Extraction”. It is also possible to accede at related news by an article (screenshot 4).

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Personally, I’ll keep this news-reading among my iPad applications because the interface gives me the freedom to discover news quickly with the Word Wall and the articles organisation by interests (i.e. Natural Langage Processing) and sub-interests (i.e. information extraction).

Some suggestions

  • Keep the links of the articles on the web to access directly at the website of a company for example
  • Delete the articles which talk about the same news (see approaches of measuring semantic similarity of texts, for example the Latent Semantic Analysis Landauer, Foltz, & Laham (1998))
  • A functionality that summarizes a recurrent news like sport results (see approaches of automatic summarization, Radev, McKeown & Hovy 2002)

Reverb is available on the App Store.

Here a video presentation of the application:








What do you think about the app? Don’t hesitate to drop your comments below.