14 Benefits of Sentiment Analysis

In other words, deep learning on a cloud is developed to explore the knowledge of complex texts for many different classes and levels [62]. After deep research in previous studies related to ensemble methods for sentiment analysis, we conclude that the ensemble technique is more efficient for sentiment analysis. The final stage is where ML sentiment analysis has the greatest advantage over rule-based approaches.

3 Other Methods

For a recommender system, sentiment analysis has been proven to be a valuable technique. A recommender system aims to predict the preference for an item of a target user. For example, collaborative filtering works on the rating matrix, what is the fundamental purpose of sentiment analysis on social media and content-based filtering works on the meta-data of the items. There’s no point in marketing if you cannot see whether you’re doing well or not. According to Statista, social media ad spend is projected to reach $450 billion in 2024.

However, it might not be very effective to reduce the variance among the component models that can make the results less reliable. The study in [56] highlighted that stacking is an efficient method of ensemble deep learning as it saves substantial training time due to the parallel training approach. Nonetheless, the use of this technique is limited because stacking can cause a problem in sentimental analysis that must work through a large volume of data, which can make stacking less efficient [54]. Thus, the use of the hybridization technique is based on the needs of data training and analysis. Boosting is another key hybridization technique that is used to combine more than one deep learning model.

Sentiment analysis challenges

Commercial software may be less accurate when analyzing texts from such domains as healthcare or finance. In 2011, researchers Loughran and McDonald found out that three-fourths of negative words aren’t negative if used in financial contexts. For these cases, you can cooperate with a data science team to develop a solution that fits your industry. The goal of this operation is to define whether a sentence has a sentiment or not and if it does, to determine whether the emotion is positive, negative, or neutral. Luckily there are many online resources to help you as well as automated SaaS sentiment analysis solutions. Or you might choose to build your own solution using open source tools.