Sentiment analysis is among the primary natural language processing (NLP) tasks and is widely utilized for extracting emotions and sentiments from text corpora. This paper proposes a comprehensive sentiment analysis approach for movie reviews based on Word2Vec, TextBlob, VADER, and Gated Recurrent Units (GRU). Word2Vec is employed for word embeddings to extract semantic word relationships for improved feature representation. TextBlob and VADER are implemented as lexicon-based sentiment analysis tools, for which TextBlob is interested in polarity and subjectivity and VADER is engineered for short texts with clear-cut sentiment indications. Besides, deep learning architecture in the form of GRU is employed for extracting long dependencies and context associations between words of text corpora for enhanced sentiment classification. Methods are experimented and contrasted on the basis of a benchmark IMDB dataset with reference to accuracy, precision, recall, and F1 score. Experimental findings substantiate that sentiment handling by deep learning-based approaches, i.e., GRU via Word2Vec embeddings, is better than traditional lexicon based approaches. The work provides insights to NLP-based opinion mining researchers and practitioners regarding the merit of utilizing hybrid approaches towards sentiment classification.