Text analytics APIs: simplified pricing

We focus a lot on unifying access to our text analytics APIs. One of such areas is pricing. We obviously want more users to have access to our systems at meaningful prices.

In the course of the last month we have unified and decreased prices for all our APIs. Here are the changes:

RSA API (entity level sentiment detection for Russian):
Overage fee for Basic plan is USD $0,02 (was: USD $0,05). This matches the overage fee on all other plans.
PRO plan is now USD $99 instead of $299.
ULTRA plan is now USD $199 instead of $350.

FUXI API (sentiment detection for Chinese):
We changed our subscription plan Basic to allow for 15,000 texts a month for just $10 instead of 500 / day.
PRO plan allows you to 100,000 texts a month for $99.

Topic API (searchable topics for texts in Russian)
Basic plan allows for sending 1,000 messages for $19. Remember, that one message can contain up to 50 texts. If you were only uploading texts you could upload 50,000 of them.

The following APIs continue to be FREE:

ConnectedWords (find semantically similar English words to the ones given)
SemanticCloud (frequency word clouds for Russian along with lemmas)

Our team is always listening to you, our users — let us know, what APIs you would like to have in addition, what features to existing APIs and what volumes you would like to handle.

Enjoy the journey of extracting signal from your textual lakes!

AI education. What market requires?

When you start looking at the field of AI (Artificial Intelligence) as a business leader or software developer you can get lost at first.

In this online seminar between Carine Simon, MIT (Boston, USA), Borys Pratsyuk, Ciklum, Valeria Zabolotna, UNIT.City (Kiev, Uktrain) and Dmitry Kan, Insider (Helsinki, Finland) you will learn:

  1. What formal AI education programs exist at MIT
  2. What industry expects of hires for AI role
  3. How to get started with AI as a practitioner — frameworks, hardware, communities

Seminar host: Misha Feldman

Hope you will enjoy the video and do let us know, if it was helpful for you!

Making sense: The API is FREE. Use it today and give us your feedback!

What if you have a bunch of or just one long text in any language and none of summarisation tools work for you?

If you have been in such a situation and also you have texts from social media or news sources that you cannot always trust in terms of how clean of noise they are, do they have URLs, hashtags, people names, addresses and so on.

And if you wanted to sift through the texts with some filter, like I want only nouns and only verbs to capture who did what.
Or I want only adjectives and nouns to capture with what colour do texts describe arbitrary objects.
Or I just want to have links out of all texts.

Now you can do all of that with one call to our SemanticCloud API.

Let’s pick the following tweet with hashtags and an URL in it and a mix of two languages: Russian and English:
Я голосую за сильного президента, за сильную независимую Россию и за тех, кто привык спрашивать только с себя, а не винить в своей лени остальных! I vote for a strong president, for a strong Russia! #выборыпрезидента #RussiaElections2018 #ЯГолосую #ЗаПутина #Putin http://pic.twitter.com/zkY8axHqZA

And let’s ask the system to output nouns, verbs, adverbs, adjectives, names, hyperlinks.

Two top words by count are: strong and сильный (translation pair)

{
“word”: “strong”,
“stem”: “strong”,
“partOfSpeech”: “Unknown”,
“count”: 2,
“lemma”: false,
“keyword”: false
},

{
“word”: “сильный”,
“stem”: “сильный”,
“partOfSpeech”: “Adjective”,
“count”: 2,
“lemma”: true,
“keyword”: false
}

But we also parsed the words out of hashtags:

{
“word”: “яголосую”,
“stem”: “яголос”,
“partOfSpeech”: “Unknown”,
“count”: 1,
“lemma”: false,
“keyword”: false
},

{
“word”: “выборыпрезидента”,
“stem”: “выборыпрезидент”,
“partOfSpeech”: “Unknown”,
“count”: 1,
“lemma”: false,
“keyword”: false
}

and a URL:

{
“word”: “http://pic.twitter.com/zky8axhqza”,
“stem”: “http://pic.twitter.com/zky8axhqza”,
“partOfSpeech”: “Hyperlink”,
“count”: 1,
“lemma”: false,
“keyword”: false
}

In addition we can ask the API to give us only top N words (by frequencies) along with lemmas (where applicable). And, more importantly, we can ask to count our secret word, that we are monitoring. Whether or not our secret word is present in the texts, it will be returned back:

{
“word”: “петербург”,
“stem”: “петербург”,
“partOfSpeech”: “Noun”,
“count”: 0,
“lemma”: true,
“keyword”: true
}

The API is FREE. Use it today and give us your feedback!

Mashape

Insider team

Feedback API to improve sentiment detection algorithm

We are pleased to announce the addition of a new feature in RussianSentimentAnalyzer API: feedback endpoint. Using the endpoint you can provide correct sentiment label for an earlier submitted text, if you disagree with the API’s label. With this information we will automatically adjust performance of the sentiment prediction after accumulating enough of ‘text,correct label’ pairs.

So from now on you can train the algorithm behind the RussianSentimentAnalyzer API!

Did you have a chance to visit our brand new web-site? Please do visit and let us know, what you think! https://semanticanalyzer.info/

Insider team

NEW API: ConnectedWords

Hello and Happy New Year!

New Year – New API. We have launched new API called ConnectedWords. We have trained a neural network using word2vec approach on a number of English texts. As input you can supply an array of keywords for which you’d like to get another list of connected or related words.

 

Available end-points:

Here is an example:

For word “launch” the API produces the following connected words:

[
“launched 0.5948931514907372”,
“ariane 0.5640206606244647”,
“icbm 0.532163213444619”,
“canaveral 0.5222400316699805”,
“rocket 0.5168188279637889”,
“launcher 0.5066764146199603”,
“suborbital 0.4987842348018603”,
“landing 0.49743730683360354”,
“expendable 0.49456818497947097”,
“agena 0.49325088465809586”,
“orbiter 0.4930563861239534”,
“shuttle 0.48127536803463045”,
“unmanned 0.47977178154360445”,
“launches 0.47013505662020805”,
“sputnik 0.4690193780888272”,
“bomarc 0.46608954818339043”,
“mission 0.4622460565342408”,
“redstone 0.4509777243147255”,
“gliders 0.4493604525398496”,
“missile 0.4388378398880377”,
“abort 0.4322835796211848”,
“rockets 0.4255249811253634”,
“lgm 0.42401975940492775”,
“launching 0.42055305756491634”,
“spacecraft 0.42044358977136653”,
“warhead 0.4203600640856848”,
“manned 0.4196165464952628”,
“skylab 0.417352627778655”,
“spaceflight 0.41261142646271765”,
“payloads 0.41167406251520333”,
“operational 0.41030200304930986”,
“refueling 0.41015588246409607”,
“orbit 0.4054650313323691”,
“extravehicular 0.4040691414909361”,
“icbms 0.4037563327101452”,
“hotol 0.4027989227897706”,
“sts 0.400049473907643”,
“saturn 0.399919637824496”,
“payload 0.398525218766963”,
“bm 0.3965859062493564”
]

How can one use the API?

1. Making your search engine smarter: expand the result set to documents containing related words. This helps you solve the issue of zero hit searches.

2. Spice up your writing. Are you a journalist / blogger / student and would like to add a flavour to your text? Send in a few words and get a set of words, that might help make your texts more interesting and engaging.

In the future we would like to add support for other languages and train on different types of texts, like social media, news, blogs etc. If you have more ideas for how to make the system more useful for your needs, get in touch!

Mashape

Fuxi API: Normalized sentiment strength (release 1.5.2)

We are pleased to announce the release 1.5.2 of Fuxi API for Chinese sentiment analysis. In this release we have bounded the sentiment strength (previously unbounded integer value) into a range [-1, 0, 1]. The value is a floating number and is normalized.

Hope you enjoy using the API & let us know any feedback / suggestions you might have!

Insider team

Fuxi API 1.4 released

We are pleased to announce the 1.4 release of the Chinese sentiment analysis Fuxi API. In this release we improved the detection quality a lot. Feel free to send us your feedback. Remember, that on-premise installations are also possible and the API is very lightweight in terms of memory and CPU consumption. 40+ companies and individuals already trust Fuxi API, we will be more than happy to see you on board!

Enjoy 1.4 release of Fuxi #api for #Chinese#sentiment #analysis on Mashape https://market.mashape.com/dmitrykey/fuxiapi and RapidAPI: https://rapidapi.com/user/dmitrykey/package/FuxiAPI