how to use google sheets add-on

1- sentiment analysis

function name
paralleldots_sentiment
description

Using the function paralleldots_sentiment you can analyze any textual content and in return get the sentiment attached to the text.
Consider the following example where the text sentence “Team performed well overall” is being analyzed using paralleldots_sentiment.

example

Using the function paralleldots_sentiment you can analyse any textual content and in return get the sentiment attached to the text.

2- keyword generator

function name
paralleldots_keywords
description

Keyword Generator API is a powerful tool in text analysis that is used to index data, generate tag clouds and accelerate the searching time. Using the function paralleldots_keywords you can generate an extensive list of relevant keywords and phrases to make research more context based.
Consider the following example where keywords are generated in the following sentence “For the Yankees, it took a stunning comeback after being down 2-0 to the Indians in the American League Division Series.”

example

Using the function paralleldots_sentiment you can analyse any textual content and in return get the sentiment attached to the text.

3- text classification (taxonomy)

function name
paralleldots_taxonomy
description

Text Classification is a very powerful tool in understanding customer behavior by categorizing data sets on a large scale. You can assign categories to a text content using the paralleldots_taxonomy function, allowing to structure data for better insights.
Consider the following example where text input “Under the Uruguay Round, the national governments of all the member countries have negotiated improved access to the markets of the member countries so as to enable business enterprises to convert trade concessions into new business opportunities.” are assigned the relevant categories using the Text Classification API.

example

Using the function paralleldots_sentiment you can analyse any textual content and in return get the sentiment attached to the text.

4- Named Entity Recognition

function name
paralleldots_ner
description

Named Entity Recognition is a very powerful tool and can identify individuals, companies, places organization, cities and other various type of entities. This is broken into three categories namely Person, Organization and place which can be called using functions pralleldots_ner_person, paralleldots_ner_organization and paralleldots_ner_place respectively.
Consider the following text input “Apple was founded by Steve Jobs in United States.” where entities are extracted using paralleldots_ner_person, paralleldots_ner_organization and paralleldots_ner_place respectively.

example

Please note in order to use the Named Entity Recognition API you must be giving the relevant type along with the function. (person, organization and place).

5- semantic analysis

function name
paralleldots_similarity
description

Semantic analysis API helps users cluster similar articles by understanding relatedness between different textual content and streamlines research by eliminating redundant text contents. Using semantic analysis (similarity) API function, paralleldots_similarity, you give two text inputs as parameters to compare and get a relatedness score out of 5. A score between 0-2.5 shows low similarity, 2.5 to 3.5 shows mild correlation and a score between 3.5 to 5 shows strong similarity.
Consider the following example where two sentences are being compared “Global warming set to exceed Paris agreement’s 1.5C limit by 2040s, according to draft UN report” and “There is a tipping point’: UN warns climate change goals laid out in Paris accord are almost out of reach”.

example

Using the function paralleldots_sentiment you can analyse any textual content and in return get the sentiment attached to the text.

6- emotion analysis

function name
paralleldots_emotion
description

Sometimes the three classes of sentiment (positive, negative and neutral) are not sufficient to understand the nuances regarding the underlying tone of a sentence. Our Emotion Analysis classifier is trained on our proprietary dataset and tells whether the underlying emotion behind a message is: Happy, Sad, Angry, Excited, or Indifferent(Other). You can use the paralleldots_emotion function to find out the emotion in any text content.
Also, you can find out the probability related to each of the underlying emotion. Use paralleldots_emotion_ <label>_probabilty to get the probability of each emotion. You can use happy, sad, angry, excited or other in place of <label>. For eg paralleldots_emotion_happy_probability will return the probability of happy emotion on the text given as input.
Consider the following eg. where the text input “I am trying to imagine you with a personality.” is being categorized using emotion detection API.

example

Using the function paralleldots_sentiment you can analyse any textual content and in return get the sentiment attached to the text.

7- intent analysis

function name
paralleldots_intent
description

This intent analysis classifier tells whether the underlying intention behind a sentence is feedback/opinion, news, query, spam or other. You can find the intent behind any text content using the paralleldots_intent function.
Also, you can find the probability related to each of the underlying intent. Use paralleldots_intent_<label>_probabilty to get the probability of each intent. You can use feedback_opinion, news, query, spam or other in place of <label>. For eg: paralleldots_intent_feedback_opinion_probability will return the probability of feedback_opinion intent on the text given as input.
Consider the following example where the text input "How do I cancel my ticket from the app?" is being categorized using intent analysis API.

example

Using the function paralleldots_sentiment you can analyse any textual content and in return get the sentiment attached to the text.

8- multilingual sentiment analysis

function name
paralleldots_sentiment
description

Understand the social sentiment of your brand, product or service while monitoring online conversations in languages other than English. Sentiment Analysis is contextual mining of text which identifies and extracts subjective information in source material. Languages supported are Arabic (ar), German(de), French(fr), Dutch(nl), Italian(it), Spanish(es), Portuguese(pt), Danish(da), Finish(fi)

Consider the following example where the text input in French "Je me sens bien après avoir couru le matin" is being categorized using multilingual sentiment analysis API.

example

Using the function paralleldots_sentiment you can analyse any textual content and in return get the sentiment attached to the text.

9- multilingual emotion analysis

function name
paralleldots_emotion
description

Sometimes the three classes of sentiment (positive, negative and neutral) are not sufficient to understand the nuances regarding the underlying tone of a sentence. Our Multilingual Emotion Analysis API is trained on our proprietary dataset and tells whether the underlying emotion behind a message is: Happy, Sad, Angry, Excited, Sarcasm or Fear. Languages supported are Arabic (ar), German(de), French(fr), Dutch(nl), Italian(it), Spanish(es), Portuguese(pt), Danish(da), Finish(fi)

Consider the following example where the text input in German "Es fühlt sich gut an nach dem Laufen am Morgen" is being categorized using multilingual sentiment analysis API.

example

Using the function paralleldots_sentiment you can analyse any textual content and in return get the sentiment attached to the text.

10- multilingual keyword analysis

function name
paralleldots_keywords
description

Our Keyword Generator algorithm supports thirteen other languages apart. Analysis of these languages can be invoked by calling the function paralleldots_keywords(text,”language_code”). Their confidence scores can be also be retrieved by appending “_confidence” at the end of each language’s function as shown in the screenshot below:

example

Using the function paralleldots_keywords you can analyse any textual content and in return get the keywords attached to the text.

security and privacy

Google Sheets add-on is built on our APIs which means that your data is processed on our servers to get the final output. We take user privacy very seriously at ParallelDots and our privacy policy can be accessed here. All the user data is stored according to our privacy policy ensuring high standards of security. However, in some cases due to contractual obligations or otherwise, user may want to keep the data in-house in which case we can deploy these algorithms on premise and build the plugin accordingly. Please send us a request to deploy these APIs on premise and any custom function that you want us to build.

Please write to us at support@paralleldots.com in case of any queries or feedback.