Tag Archives: api

DocTop API: new release

Dear DocTop API users!

We have released an improvement to how we handle topics for texts with hastags and @ handles.

via GIPHY

Here is an example:
Text: The Apache ecosystem could benefit from your knowledge! CfP for #ACNA2020 @apache

“topics”: [
“knowledge”,
“cfp”,
“apache ecosystem”,
“#ACNA2020”,
“@apache”
]

Insider team
Check out the API here:

https://rapidapi.com/dmitrykey/api/doctop/endpoints

Array of texts and quality improvements / Массив текстов и улучшения качества

Hello and Happy New Year!

(на русском — читайте ниже)

We are happy to let you know of three major changes to the RSA API for entity level sentiment analysis of Russian texts:

Mashape

You can now send in an array of up to 10 texts. Use the new end-point: https://russiansentimentanalyzer.p.mashape.com/rsa/sentiment/polarity/jsons/

Example of input with two texts:
 
[
  {
    "text": "Гиперответственный классный исполнитель :)\nОтдельный респект за подхваченное в 22-00 задание!",
    "article_id": 1,
    "include_strength": true
  },
  {
    "text": "быстро доставил,но претензии остались",
    "article_id": 2,
    "include_strength": true
  },
  {
    "text": "погода отличная"
  }
]

Response from the API will have polarity labels tagged with original article_id values, if provided (otherwise follows the input order of texts):

  
[
    {
        "sentiment": "POSITIVE",
        "strength": 1,
        "article_id": "1"
    },
    {
        "sentiment": "NEUTRAL",
        "strength": 0,
        "article_id": "2"
    },
    {
        "sentiment": "POSITIVE",
        "strength": 1
    }
]
  1. We have tuned the quality for both positive and negative tonality.
  2. We are back to Fremium model allowing you to send 100 texts a month for free.

Enjoy!

Insider team

Привет и с Новым годом!

Мы рады сообщить о трёх важных улучшениях в RSA API — системе объектного анализа тональности текстов на русском языке:

  1. Теперь за один запрос можно прислать до 10 текстов. Используйте новый энд-пойнт: https://russiansentimentanalyzer.p.mashape.com/rsa/sentiment/polarity/jsons/
    Пример запроса:
 
[
  {
    "text": "Гиперответственный классный исполнитель :)\nОтдельный респект за подхваченное в 22-00 задание!",
    "article_id": 1,
    "include_strength": true
  },
  {
    "text": "быстро доставил,но претензии остались",
    "article_id": 2,
    "include_strength": true
  },
  {
    "text": "погода отличная"
  }
]

Ответ системы будет содержать оригинальные article_id либо следовать изначальному порядку текстов:

  
[
    {
        "sentiment": "POSITIVE",
        "strength": 1,
        "article_id": "1"
    },
    {
        "sentiment": "NEUTRAL",
        "strength": 0,
        "article_id": "2"
    },
    {
        "sentiment": "POSITIVE",
        "strength": 1
    }
]
  1. Было улучшено качество распознавания позитивной и негативной тональности.
  2. Мы вернули модель Fremium, позволяющей присылать до 100 текстов в месяц бесплатно!
Mashape

Команда Insider

Fuxi API 1.2 for Chinese sentiment analysis is here

Analyzing Sina Weibo (Chinese Twitter) and Renren (Chinese Facebook) for sentiment are quite tricky. In general social media analysis, for instance for Russian is tricky. There are few reasons:

  1. Grammar: in short messages there is not much space to spell out correct grammar. So in most cases it is “broken” from the stand point of classic parsers.
  2. Words: they change frequently, following social media development of a particular news / reaction or may be even a flash mob.
  3. Sarcasm: the author does not mean the sentiment you deduce by reading it for the first time. It sometimes takes a research and find a visual item, that helps understand the sentiment:     

Fuxi API is catching up with what’s cooking in Chinese social media by analyzing a vast array of messages in Simplified and Traditional Chinese. We have just released its 1.2 version with a number of changes to better tune for the sentiment signal in the avalanche of tweets, blog posts and news articles, all in Chinese. Check it out.

Annotating sentiment with RussianSentimentAnalyzer API in Java

Hello!

In this post we will show how easy it is to start using RussianSentimentAnalyzer API on mashape from your Java code.

package com.semanticanalyzer;

import com.mashape.unirest.http.HttpResponse;
import com.mashape.unirest.http.JsonNode;
import com.mashape.unirest.http.Unirest;
import com.mashape.unirest.http.exceptions.UnirestException;

public class RussianSentimentAnalyzerMashapeClient {

    private final static String mashapeKey = "[PUT_YOUR_MASHAPE_KEY_HERE]";

    public static void main(String[] args) throws UnirestException {

        String textToAnnotate = "'ВТБ кстати неплохой банк)'";
        String targetObject = "'ВТБ'";

        // These code snippets use an open-source library. http://unirest.io/java
        HttpResponse response = Unirest.post("https://russiansentimentanalyzer.p.mashape.com/rsa/sentiment/polarity/json/")
                .header("X-Mashape-Key", mashapeKey)
                .header("Content-Type", "application/json")
                .header("Accept", "application/json")
                .body("{'text':" + textToAnnotate + ",'object_keywords':" + targetObject + ",'output_format':'json'}")
                .asJson();

        System.out.println("Input text = " + textToAnnotate + "\n" + "Target object:" + targetObject);
        System.out.println("RussianSentimentAnalyzer response:" + response.getBody().toString());
    }
}

In the code snippet above we’ve used the mashape’s Unirest API, that makes HTTP requesting in Java super easy.

All you really need to care about is to register at mashape.com, sign up for RussianSentimentAnalyzer API and insert your unique mashape key into the code, in place of “PUT_YOUR_MASHAPE_KEY_HERE”, as a value of the mashapeKey variable.

If everything has been set right, execute the code and you should see the following output:

Input text = 'ВТБ кстати неплохой банк)'
Target object:'ВТБ'
RussianSentimentAnalyzer response:{"sentiment":"POSITIVE","synonyms":"[ВТБ]"}

Now you can easily hook the API up into your cool Java app and annotate texts in Russian for sentiment!

You’ll find the code on our github here: https://github.com/semanticanalyzer/nlproc_sdk_sample_code

Keep calm and use an API

Sentiment detection for English: cheaper prices for even more benefit

At SemanticAnalyzer we believe natural language processing APIs should become a commodity. In a good sense. Every developer should be able to afford integrating AI into their cool mobile and web applications.

So we decided to substantially lower the prices for our English sentiment detection API SentiFindr. New prices you will find here:

https://www.mashape.com/dmitrykey/sentifindr/pricing

We always welcome your feedback. Integrate now for free and tell us what you think! Just raise a ticket anytime: https://www.mashape.com/dmitrykey/sentifindr/support

 

… and: Keep calm and use an API;

Bridge in Helsinki

 

Russian Sentiment Analyzer API: pricing

We have just published the Russian Sentiment Analyzer API on mashape!

The pricing is pretty straightforward, feel free to give your feedback or request a custom plan.

RussianSentimentAnalyzerPricing

 

You will need to register with mashape in order to start consuming the API.

To get started, click this little button:

RussianSentimentAnalyzer API

JSON API анализа тональности на русском языке

На основе технологического стека SemanticAnalyzer мы запустили API анализа тональности на русском языке. Это json API, принимающий следующую структуру:

{
 "text":"some_text_in_utf-8",
 "object_keywords":"object_keywords_in_csv_in_utf-8",
 "output_format":"json or xml"
}

API синхронно выдаёт json либо xml со структурой:

json:

{
 "sentiment": "${sentimentTag}",
 "synonyms": "${synonyms}"
}

xml:

<!--?xml version="1.0" encoding="utf-8"?-->
 
  ${sentimentTag}
  ${synonyms}
 

Пример с реальным текстом:

{
 "text":"Самарские пиармены помогут уральскому самородку:
    Засекин.Ру – самарские новости и мнения экспертов #ИгорьХолманских",
 "object_keywords":"ИгорьХолманских,Игорь Холманских",
 "output_format":"json"
}

Ответ системы:

{
 "sentiment": "POSITIVE",
 "synonyms": "[ИгорьХолманских]"
}

Ответ содержит метку тональности и объект, по отношению к которому она была вычислена.

Также системой поддерживаются POST запросы со стандартным набором параметров. В этом случае в тело POST запроса передаётся urlencoded key=value строка в http формате:

text=my_text&amp;object_keywords=keyword1,keyword2,keyword3&amp;output_format={json}.

К API прилагается документация, а также примеры интеграции на Java, Node, PHP, Python, Objective-C, Ruby и .NET.

Получить доступ к API:

RussianSentimentAnalyzer API