In every city, harassment and violence becomes one of the major problems for women. Further, women’s personal life is suffered by the bullying and abusive content presented in online social networking (OSN). Therefore, it is necessary to identify the women safety in OSN environment. However, the conventional methods failed to predict the maximum safety analysis. So, this work is focused on women safety prediction using decision tree (WSP-DT) classifier. Initially, twitter dataset is considered to implement the entire system, which is then pre-processed to remove the missing and unknown symbols. Then, natural language toolbox kit (NLTK) applied to perform the tokenization, conversion to lower case, stop words identification, stemming and lemmatization of tweets. Then, text blob protocol is developed to identify sentiments of pre-processed tweets, which identifies the positive, negative and neutral polarities of tweets. Further, term frequency-inverse document frequency (TF-IDF) is applied to extract the data features based on word and character frequency. Finally, decision tree classifier applied to identify the fake or genuine tweet based on multi-level training. The simulations conducted on twitter dataset show that the proposed WSP-DT classifier resulted in superior performance than the other methods.