how to use excel add-in
introduction

Our Excel Add-in provides the state-of-the-art Natural Language Processing capabilities to your analysis without the need to write code (which you need in case you want to use the APIs).

The Add-in also comes in handy when you need to run analysis in batches and discover insights in them (tweets from a social campaign, earnings call transcripts, open-ended user feedback etc.). You can export all your data from any BI tool you use in xlsx (or csv format) and install our plugin to annotate the data with sentiment, emotions, intent etc. and analyse them from the comfort of your spreadsheet in seconds.

For installation guide, click here.

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 “These brownies are the worst in the world.” 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- multilingual sentiment analysis

Sentiment Analysis can also be done in different languages namely Spanish, Portuguese and Chinese. It works the same way as our Sentiment Analysis works.

spanish

function name
paralleldots_multilang_es

Portuguese

function name
paralleldots_multilang_pt

chinese

function name
paralleldots_multilang_cn
description

In the functions es, pt and cn are the language codes for Spanish, Portuguese and Chinese respectively. The only difference in using sentiment analysis and multilingual sentiment analysis is that now you get the confidence score as your probability. To get the confidence score you can add _confidence in each of the functions like “paralleldots_multilang_es_confidence” will get you the confidence score of the Spanish sentence that you give as input. It varies between 0 and 1, the closer the number is to 1, as accurate is the analysis.
Consider the following example where Barcelona, Lima and Beijing are praised for being beautiful in their native languages.

example

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

3- 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.

4- 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 “AN OUTBREAK of pneumonic plague - commonly known as the Black Death - has killed at least 45 people and could become an epidemic.” 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.

5- 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 “When Michael Jordan was at the peak of his powers as an NBA superstar, his Chicago Bulls teams were mowing down the competition, winning six National Basketball Association titles and setting a record for wins in a season that was broken by the Golden State Warriors two seasons ago.” 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).

6- 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 “Michael Jordan is the best player in the world” and “Lionel Messi is the best player in the world”.

example

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

7- 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 “The latest Porcupine Tree song is rocking!” 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.

8- 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 "Michael Jordan on NBA parity: 28 teams 'are going to be garbage'" 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.

security and privacy

Excel add-in 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.