New Features: Boldo Update

Photo by Praveen (CC BY 2.0)

Following the requests of our users, we implemented additional features. They are now active; feel free to kick the tires. (And yes, our updates are named after herbal teas.)

Detecting Attempts to Establish External Contact

In some communities, external contacts must be monitored. Marketplaces, communities with some kind of harassment issues, scammers attempting to lure users out, and so on. As of today, a common simple solution is to scan messages using regular expressions and find phone numbers and emails. That, however, is insufficient, as the users often find ways to bypass these checks, or introduce non-standard formatting.

We now detect these attempts and place the detected snippets with the external_contact type. For example, a request to provide an email, a WhatsApp number, and so on (“we need your email”, “wat is ur whats app”, etc.).

Signal to Noise Ranking

If you need to create a summary or write a report about how a particular topic or brand is reflected in the social media, the sheer amount of posts that need to be processed is often overwhelming. What’s worse, 95% of these are not much help. They either copy other people’s thoughts, are completely off-topic, contain all kinds of abuse, or just vent frustration and negative emotions. Same goes for the comments: with a few pearls, many are just background noise.

The signal to noise ratio is not unlike conventional search engine rankings, but better adapted for the social media content needs.

The ranking prioritises posts related to the specified concepts and domains, and penalises off-topic content and abuse.

In order to compute the signal to noise ranking, provide an array of concept IDs (family IDs) in your settings under the relevant attribute (e.g. “relevant”: [12345,6789]).

Native Topic Standard Overhaul

While we support taxonomy standards like IPTC and IAB, our internal taxonomy is much richer. The topics that don’t appear in IPTC and IAB can be exposed using the native topic mode (code: native). Previously, it was used for internal purposes only, and contained numeric codes.

After this update, they contain English descriptions, and the taxonomy was also expanded.

Topic Optimization

Some of the topics may overlap. “Compound” topics like cryptocurrency may imply other topics like finance and software. Depending on your application, you may or may not need these “constituent” topics.

The optimize_topics parameter allows control over how it’s presented. For example, when analyzing a sentence like “exchange btc to xmr”, and the optimize_topics is set to false, we get:

  {
    "text": "exchange btc to xmr",
    "topics": [
       "money",
       "commerce",
       "business",
       "finance",
       "software",
       "currency",
       "cryptocurrency"
    ]
  }

When the parameter is set to true, we get:

  {
    "text": "exchange btc to xmr",
    "topics": [
       "cryptocurrency"
    ]
  }

Format-Sensitive Logic

We had to learn the hard way that it matters where the text is coming from.

A simple example. A single word like “fool” may be a title, in which case it bears a negative connotation, but not a personal attack. However, when posted as a part of a dialogue (e.g. in a comment in Instagram), it is a personal attack.

We introduced support of different logic for different formats.

Feature Default Format Changed to Universal Dependencies

Tisane supports several standards to display grammar features, such as Penn, Universal Dependencies, Glossing Abbreviations, and the native codes and descriptions. We saw that the original glossing abbreviation format was confusing for many users, and changed the default to Universal Dependencies.

If you prefer to do so, you can still use “glossing” to obtain glossing abbreviations.

Document-Level Sentiment

While we stress that the aspect-based sentiment analysis provides more actionable intelligence, we added a document-level attribute for certain scenarios. Add “document_sentiment”:true to the settings to obtain the document-level sentiment value in range -1 (most negative) thru 1 (most positive). It will be placed in the sentiment attribute.

Contact us for questions and more information. If you are new to Tisane, please sign up here, it’s free.

Tisane Labs Launches Solution to Detect Hate Speech and Cyberbullying

published on Yahoo Finance via PRNewswire

Affordable API enables developers and businesses to detect hate speech, cyberbullying, unwanted sexual advances, criminal activity, and more

WASHINGTON, Nov. 13, 2018 /PRNewswire/ — Tisane Labs, a supplier of text analytics AI solutions, today announced the launch of Tisane API, the first API to detect and classify abusive textual content in 27 languages. Tisane detects hate speech, personal attacks, unwanted sexual advances, and criminal activity in text, with additional varieties of detected abuse to come.

“Trolls, bigots, harassers, and criminals made the Internet an unpleasant and at times dangerous place. For the users, it often means being unsafe online with possible consequences in real life. For the online communities, it means high user turnover, additional headaches with the moderation, and enormous monetary losses or legal issues,” said Vadim Berman, Chief Executive Officer and Co-founder of Tisane Labs. “Now, with Tisane API, the communities online can automate much of the moderation process and even warn potential offenders before the post is published. Rather than producing a blanket statement and a floating-point figure, Tisane API pinpoints the actual instance of abuse and classifies the type of abuse.”

Tisane API runs in the cloud, with a simple REST interface that can be linked from any popular programming platform today. Tisane Labs provides a range of plans for every pocket with the option of a custom installation on premises and a generous FREE plan.

To try Tisane API, visit https://tisane.ai.

For more information, contact Carla Johnston (email: Carla.Johnston@tisane.ai or call: +1 (703)-628-8827)

Related Links

Tisane Labs website

Tisane Labs launches Tisane API

Tisane Labs is pleased to announce the release of Tisane API.

Harness the power of next-generation AI to extract more from text in 27 languages: detect hate speech, sexual harassment, cyberbullying, extract topics, and find not only whether, but also why the customer is happy or unhappy with your product or service. Our applications and components are accessible in the cloud on a subscription basis (SaaS), can be installed on premises, or embedded in 3rd party applications for seamless integration and security.

We support: English, Chinese (Simplified and Traditional), Arabic, Danish, German, Spanish, Persian, Finnish, French, Hebrew, Indonesian, Italian, Japanese, Korean, Malay, Dutch, Norwegian, Polish, Pashto, Portuguese, Russian, Swedish, Thai, Turkish, Urdu, Vietnamese.

We offer several ways to use our components, from a generous free plan (not a limited trial) to enterprise-grade plans and on prem installation options. Whether you’re a small startup, an independent developer, or an enterprise, we can work together.

Questions? Browse our knowledge base, chat with us using the real-time chat widget, or email us.

Sign up and start using Tisane API today.