Abstract:
"The research suggests a content-based job recommendation system to solve this issue by
calculating the employer's sentiment score using sentiment analysis methods. The sentiment
score is determined using reviews and ratings submitted by the employer company's current
and former employees. The suggested system makes use of NLP techniques like data
preprocessing, Vader Lexicon Score calculation, word-based sentiment analysis, and a Support
Vector Machine Classifier model. The model was chosen from a pool of artificial intelligence
algorithms trained on the English Google News 7B corpus. In order to rank job
recommendations, the sentiment score is then combined with the similarity matrix and pairwise
distance to calculate a weighted ensemble score.
Based on user evaluations and the evaluation matrix for the developed algorithms, the
prototype of the proposed system produces usable results. This research has successfully
implemented a novel approach that integrates sentiment analysis to check employer credibility
in job recommendation systems. The proposed system offers useful information that helps job
seekers choose their careers wisely."
