Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Contextual Emotion Classification in Text Using Hybrid Word2Vec-BiLSTM

Contextual Emotion Classification in Text Using Hybrid Word2Vec-BiLSTM

Duvvuri, Suneel Kumar
Sivasrinu, Nilla

Paperback

Technology & Engineering

ISBN10: 6209879535
ISBN13: 9786209879531
Publisher: LAP Lambert Academic Publishing
Published: Apr 17 2026
Pages: 116
Weight: 0.36
Height: 0.28 Width: 6.00 Depth: 9.00
Language: English
In the modern digital era, human communication has shifted significantly toward unstructured text found on social media, micro-blogs, and instant messaging platforms. While traditional face-to-face interactions rely on non-verbal cues like facial expressions and vocal tone, digital text lacks these signals, making the detection of emotional intent a complex challenge for Natural Language Processing (NLP). This study addresses the limitations of traditional machine learning methods, such as Naïve Bayes and Logistic Regression, which often fail to capture contextual meaning and long-range sequential dependencies. The proposed research introduces a hybrid deep learning framework that integrates Word2Vec(Continuous Bag-of-Words) embeddings with a Bidirectional Long Short-Term Memory (BiLSTM) network. Word2Vec is utilized to extract dense semantic features, enabling the model to understand mathematical relationships between words.

Also in

Technology & Engineering

View All