Natural Language Processing, or NLP, is a discipline of computer science that uses computer-based methods to analyze language in text and speech. The purpose and ambition of NLP is to enable computers to converse quickly and easily. Emailing, texting, and cross-language communication are all used in our daily lives. Sentiment analysis, also known as opinion mining, is a branch of natural language processing that mines a text to extract subjective information from the source material, allowing a company to better understand the social sentiment surrounding their brand, product, or service by monitoring online conversations. Nowadays, in the era of social media, firms' service and products have a significant impact on customer input and feedback. Customers freely express their feelings about a company's service and products on the company's social media pages. Companies face a significant and tough problem in extracting meaningful information from this unstructured, unorganized, massive, and fragmented data. Amhara Media Corporation is one victim of this and sentiment analysis is used for resolving such issues, it allows businesses to quickly learn what customers think and feel about their service or product. Sentiment analysis can be performed in Document level, Sentence level and Aspect level. Document level sentiment classifies the whole document into positive negative or neutral but the customer opinion need may not in that manner. Hence the negativity or positivity of a single sentence does not measure the entire document as a whole. Sentence level sentiment analysis also categorizes the entire sentence as negative, good, or neutral, although consumer feedback may not be in those categories. Many publications have been written on Amharic sentiment analysis, however none of them have looked at the aspect level or used a deep learning approach. This research focuses on sentiment analysis utilizing aspect level with a hybrid deep learning approach. The Dataset were collected from in Excel format from the Amhara media corporation's official Facebook page. To improve the quality of the data, the researcher applied tokenization, stop word removal, Emoji, punctuation, eliminating non-Amharic texts, word embedding, and normalization and converting English language comments to Amharic and utilize comment exporter software to collect 10,000. The researcher used CNN, LSTM, Hybrid CNN with GRU and hybrid CNN with LSTM. Finally, Hybrid CNN with GRU is selected as best approach due to the better accuracy it registered. Keywords: Sentiment Analysis, Aspect Level, Amharic, Comment