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DESCRIPTION:The rise of digitalization and the advent of social media and e
 -commerce have generated an abundance of data than before. Natural Languag
 e Processing (NLP) is a significant branch of artificial intelligence that
  helps the machine interpret human languages and perform the desired task 
 by analyzing the semantics\, content\, and pattern. Sentiment analysis is 
 the most common technique in Natural Language Processing used to determine
  the underlying sentiments of a text. This technique is currently in place
  for different Business Organizations to analyze their brand’s market va
 lue\, brand reputation\, and customer perception of new brand/new change. 
 Businesses use social media channels to cater to their customer service\, 
 and people use social media to express/share their wide range of opinions 
 or experiences about a product/brand. These opinions and experiences refle
 ct the real-time sentiments of a customer. Sentiment analysis will help bu
 sinesses designing an effective marketing campaign\, better customer satis
 faction\, boost sales\, help improve customer experience\, understand cust
 omer perception to change and the brand’s market reputation. The custome
 r views expressed on Twitter\, Facebook\, and other online forums are form
 ing the base of customer strategy for brands worldwide. Businesses are opt
 ing to shift their traditional customer feedback analysis method to text c
 lassification since people prefer to post the genuine reviews on the inter
 net. Analyzing the underlying sentiments in the text will help the busines
 s to understand their customers' voices and their brand reputation in the 
 market in real-time. Sentiment analysis will help the businesses designing
  an effective marketing campaign\, better customer satisfaction\, boost sa
 les\, help improve customer experience\, understand customer perception to
  change and the brand’s market reputation. Twitter sentiment analysis ai
 ms to classify text into positive/negative based on its underlying semanti
 cs.\n\nSpeaker(s): Akhil Mathew\, \n\nToronto\, Ontario\, Canada\, Virtual
 : https://events.vtools.ieee.org/m/312338
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.o
 rg/m/312338
ORGANIZER:reza.dibaj@ieee.org
SEQUENCE:4
SUMMARY:Sentiment Analysis on Twitter Data – Students Research in ML and 
 DL at Durham College
URL;VALUE=URI:https://events.vtools.ieee.org/m/312338
X-ALT-DESC:Description: <br /><p>The rise of digitalization and the advent 
 of social media and e-commerce have generated an abundance of data than be
 fore. Natural Language Processing (NLP) is a significant branch of artific
 ial intelligence that helps the machine interpret human languages and perf
 orm the desired task by analyzing the semantics\, content\, and pattern. S
 entiment analysis is the most common technique in Natural Language Process
 ing used to determine the underlying sentiments of a text. This technique 
 is currently in place for different Business Organizations to analyze thei
 r brand&rsquo\;s market value\, brand reputation\, and customer perception
  of new brand/new change. Businesses use social media channels to cater to
  their customer service\, and people use social media to express/share the
 ir wide range of opinions or experiences about a product/brand. These opin
 ions and experiences reflect the real-time sentiments of a customer. Senti
 ment analysis will help businesses designing an effective marketing campai
 gn\, better customer satisfaction\, boost sales\, help improve customer ex
 perience\, understand customer perception to change and the brand&rsquo\;s
  market reputation. The customer views expressed on Twitter\, Facebook\, a
 nd other online forums are forming the base of customer strategy for brand
 s worldwide. Businesses are opting to shift their traditional customer fee
 dback analysis method to text classification since people prefer to post t
 he genuine reviews on the internet. Analyzing the underlying sentiments in
  the text will help the business to understand their customers' voices and
  their brand reputation in the market in real-time. Sentiment analysis wil
 l help the businesses designing an effective marketing campaign\, better c
 ustomer satisfaction\, boost sales\, help improve customer experience\, un
 derstand customer perception to change and the brand&rsquo\;s market reput
 ation. Twitter sentiment analysis aims to classify text into positive/nega
 tive based on its underlying semantics.</p>
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