## 1 Introduction  
Natural language processing (NLP) has become a popular research topic in the last two decades. Its popularity has attracted researchers to examine data, web and text mining, as well as information retrieval [1]. Opinion mining is one attractive topic, among other topics such as machine translation, customer support boss, text summarization, and speech recognition. Opinion mining is essential in current online interactions, such as e-commerce, e-government, and many online services, for understanding public opinion on specific issues [2]. Governments worldwide increasingly adopt social media to engage their citizens and understand citizen opinion with respect to public policy [3]. Opinion mining can mainly be divided into lexicon-based and supervised machine learning approaches. Lexicon-based approaches rely on the sentiment word dictionary. To carry out lexicon-based sentiment analysis, lexicon dictionaries are available for public use, such as wordnet in English, [4] in Bahasa, and [5; 6; 7; 8] in Arabic. On the other hand, the supervised approach needs manually labeled corpora to train the classifier. Researchers are working on lexicon-based opinion mining in many languages, such as [9] for the Indonesian language, [6; 7; 8; 9; 10] for Arabic, and [11] for Urdu. The supervised approach has attracted researchers in detecting sentiment levels using SVM, ANN, Naive Bayes, Decision Tree, Random Forest, and numerous exercises with many different languages and datasets. Currently, researchers are moving forward in implementing deep learning for natural language processing in general, specifically with respect to sentiment polarity classification. The computing power available in current hardware and cloud services allows researchers to solve problems using heavy computing algorithms. This capability has enabled a massive development in deep learning as an extension of the neural network concept. Deep learning has made a successful breakthrough in many recognition tasks for image, video, and text. In particular, language signals have specific properties regarding time sequence. The early research on textual recognition assumed that the sequence had no significant effect. This assumption led to a lower recognition rate for early sentiment classification using time-series-unaware algorithms such as Artificial Neural Network, Support Vector Machine, and Naive Bayes. Long Short-Term Memory (LSTM) extends an artificial neural network with a connection between an earlier neuron and the next neuron. Because the neuron in LSTM does not only contain a single operation, it is called an LSTM-cell instead of a neuron. With LSTM architectures, if the sentence in a corpus consists of five words, the first word will come into the first unit, while subsequent words will go to the second through fifth units. To maintain the contribution of the first LSTM unit in the computation of the second unit, the output of the first unit needs to be connected to the second unit. This connection is maintained between subsequent calls. LSTM ensures that word signals in a sentence affect each subsequent word. Therefore, LSTM is suitable for the computation of time series problems, textual and time series prediction analysis, sound, and voice computation. With respect to problem construction, this paper discusses the implementation of LSTM for sentiment analysis. An extensive dataset is needed in a deep learning environment to enable the necessary training to obtain the pattern of the problem. Deep learning implementation in image and video processing has benefited from the huge datasets available to the public.