Traditional text-based sentiment analysis often falls short of understanding the emotional context of social media material because social media platforms like Twitter blend text and graphics. This work employs a multimodal sentiment analysis technique that combines textual and visual data to improve sentiment classification in order to get around these limitations. The first method effectively identifies objectionable content on Twitter by using Convolutional Neural Networks (CNNs) for picture analysis and Long Short-Term Memory (LSTM) networks for text feature extraction. Building on this, a cross-attention-based fusion model is used to further enhance sentiment classification into positive, neutral, and negative categories. This latest model interprets text in using an ALBert and BiLSTM networks and evaluates images through ResNet and Convolutional Block Attention Module (CBAM). Its cross-attention mechanism is able to make more complex and accurate sentiment analysis through coordination with emotional information generated from each different sense. These results demonstrate the power of advanced fusion techniques in multimodal sentiment analysis to reach a significantly richer and more contextualized interpretation of the sentiment in social media than purely text-based approaches.