This study aims to identify various factors that influence language choice in communication, as well as to analyze how the use of appropriate language can have a positive impact on the success of message delivery. The method used is qualitative research with a descriptive approach. Data were collected through documentation techniques and analyzed using thematic methods. The results of the study indicate that success in delivering messages is greatly influenced by language choice, which depends on a number of factors, such as communication context, communication objectives, audience background and characteristics, media or communication channels, and relationships between speakers. The selection of appropriate language has been shown to improve message clarity, build emotional connections, strengthen the credibility of the communicator, avoid misunderstandings, and increase the appeal and memorability of the message. These findings emphasize the importance of choosing the right language in the communication process so that messages can be delivered effectively and easily understood as they should.

Twitter, a social media platform with millions of users, serves as a valuable source for unique insights. The case of Lestibillar domestic violence has garnered attention, fueling various circulating rumors that encompass positive, negative, and neutral opinions. This, in turn, gives rise to the potential spread of fake news. To counter this, sentiment analysis is employed using machine learning techniques. In this research, two machine learning algorithms within the realm of supervised learning are compared: lexicon-based and Naive Bayes. Sentiment objects are created for each algorithm to facilitate the comparison, aiming to determine which algorithm performs better in terms of accuracy. The results of the calculations indicate that Naive Bayes outperforms, achieving a superior accuracy of 99.96%, while the lexicon-based method lags significantly behind at 10.29%. The dominance of positive tweets is evident, comprising 2709 out of the total tweets on Twitter.