Key features that are essential in a sentiment monitoring tool are multilingual efficacy, precise aspect-based sentiment analysis, named entity recognition, and an effective visualization dashboard. The following list has more details on the features and benefits you should look at, if you are in the market for a sentiment analysis tool.

1. Will the Future of Search
be Semantic in 2021?
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2. Table of Contents
● What is Sentiment Analysis?
● What are key sentiment analysis features?
● An API with the best sentiment analysis features

3. 10 Features of sentiment
analysis To Look Out For
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4. Sentiment analysis is a type of data analytics that deals specifically
with text that expresses various forms of emotions - from love to
hate. An emotion mining API with its precision-laden sentiment
analysis features prompts the digital transformation of organizations
through their big data. Big data needs to be leveraged and so it must
be mined for insights that companies can use to formulate solutions
and strategies for growth. After all, to be data-driven does not just
mean data-gathering but truly understanding it as well.

5. What is Sentiment Analysis?
Some of the most useful insights are opinions and feelings that
customers and consumers express about their purchase journey
and experience. The process of extracting and scoring this type of
customer data is called sentiment analysis or sentiment mining.
When applied, sentiment analysis rates chunks of such text data
as positive, neutral or negative. It empowers you to understand
your customers, employees, or patients better - regardless of your
industry or countries. This is how a good multilingual sentiment
analysis software can help boost your business intelligence and
build brand reputation.

6. What are key sentiment analysis
features?
Key features that are essential in a sentiment monitoring tool are
multilingual efficacy, precise aspect-based sentiment analysis,
named entity recognition, and an effective visualization dashboard.
The following list has more details on the features and benefits
you should look at, if you are in the market for a sentiment
analysis tool.

7. How is sentiment analysis on reviews done?
1. Data Gathering:
Gather and prepare your data that you want to analyze whether it’s internal (customer feedback) or external data (reviews from
rating sites). To prepare the data for text analysis, all you need to do is put it into a CSV or XLS document format.
2. Apply Review Sentiment Analysis API:
Run your input data through your sentiment analytics API. It will quickly return sentiment scores for each relevant review topic,
aspect, or entity ranging from -1 for negative emotions, 0 for neutral feelings, and 1 for positive sentiment.
3. Sentiment Analysis Dashboard:
Once you receive the sentiment scoring, you can use various different data visualization tools like Power BI, Tableau, or
Repustate’s Sentiment analysis dashboard to quickly turn your data into visual reports. These reports are made up of charts, graphs
and tables to discover trends, patterns, and actionable insights in your data.

8. An API with the best sentiment analysis
features
An API with the best sentiment analysis featuresRepustate’s sentiment
analysis engine is supported by the world’s most efficient named entity
recognition (NER). The intuitive sentiment analysis solution comprises all
the top ten sentiment analysis features noted above and is driven by its
NER that is the foundation of the engine’s deep semantic search
capability. Because of this, the model gives a stellar performance in the
four most important areas - accuracy, granularity, languages, and speed.
The very fact that our sentiment analysis API supports more than 23
languages natively, tells you how painstakingly the corpus for each
language has been manually curated and tagged by our data scientists
over a decade, and the model trained to achieve more than an 85%
accuracy rate.

9. Thank you!
Understand your data,
customers, & employees with
12X the speed and accuracy.
Visit: www.repustate.com to
learn more