Email Sentiment Analysis using Splunk NLP Toolkit

 Syed Qutb      12/10/2023 10:30      114

Since mid-90’s, modes of correspondence and communication have tremendously evolved to services like real-time chat rooms, blogs, discussion forums and plethora of social networking sites. However, despite emerging communication means, E-mail channel is still considered to be a reliable platform which is easy to use, fast, asynchronous in nature and keeps a searchable record of correspondence. Hence, majority of organizations and government bodies heavily rely and prefer using e-mails for their routine business communication.

Nearly, 3.9 billion users globally, use electronic mail every day in their respective homes and offices making it an indispensable part of our daily life. Where it is the most common way of business workspace communication, it also provides means for cyber-bullying, defamatory remarks and exchanging of inappropriate words over internet which is detrimental to any business. Naturally, observing all correspondence and characterizing them into categories is humanly impossible. This is where Sentiment Analysis, a machine learning tool, sub-field of Natural Language Processing (NLP) plays a pivotal role in identification and categorization of emotion expressed within the body of an e-mail.

Keeping the above problem statement in view, we have implemented Sentiment Analysis of E-mails in our Splunk app using NLP Text Analytics. After cleaning the body content with regular expression, we have cleaned the text with Beautiful Soup4 wrapper to remove html/xml tags and then we carried out lemmatization of the content.

VADER Analsyis

Finally, the content is passed through Valence Aware Dictionary for Sentiment Reasoning (VADER) which relies on a dictionary that maps lexical features to emotion intensities commonly referred as sentiment score. The scores are segregated into headings of Neutral, Positive and Negative according to their semantic orientation. This score ultimately helps in defining of entire Sentiment Analysis of an E-mail. List of common Positive and Negative words by VADER are defined as under: -

  • Positive Words: Archive, Adore, Love, Happy, Enjoy
  • Negative Words: Hurt, Ugly, Sad, Worse

The Splunk's SPL query used for Sentiment Analysis in our EUNOMATIX MLDETECT app is appended as under: -

For further details and functionality of our ML based detection framework and SPL queries,
please contact EUNOMATIX, info@eunomatix.com.

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Established in 2012, EUNOMATIX is fast-paced, growing company that is committed to innovation, excellence and provide state of the art network and security solutions to their clients. EUNOMATIX has a track record of quality service to companies across the US, UK, Europe and Middle East.

Our out-of-the-box and proactive security approach gives customer the capability to reduce their OpEx and CapEx through a systematic security implementation plan. A list of customers currently engaged with us for Managed Security Operations, Machine Learning Analytics and Threat Hunting include companies mainly from government, defence, telecommunication and health sectors. However, we at EUNOMATIX also provision services for the university research labs and networks as these comparatively more challenging in terms of technology and rich feature perspectives.