The word “Social” has taken a whole new meaning in today’s digital era. Simply going out to enjoy is no longer the only “social” criteria. Social now is — giving a peek in your personal and professional life to your connections. Facebook, Twitter, Instagram, and other leading platforms have connected people in ways that were unimaginable 20 years ago. More than that, these platforms have become an excellent resource for businesses to reach new customers, understand the perception of their brand, seek feedback, and improve customer experience. Today, analytics has become a driving force for businesses, enabling them to use the power of data to grow user base and revenues.

在當今的數字時代，“社交”一詞具有全新的含義。 單純地出去享受不再是唯一的“社交”標準。 現在的社交活動-窺視您的個人和職業生活中的人際關系。 Facebook，Twitter，Instagram和其他領先平臺以人們20年前無法想象的方式聯系人們。 不僅如此，這些平臺已成為企業吸引新客戶，了解其品牌認知，尋求反饋并改善客戶體驗的絕佳資源。 如今，分析已成為企業的驅動力，使企業能夠利用數據的力量來擴大用戶群和??收入。

情感分析到底是什么？ (What exactly is Sentiment Analysis?)

One such power, Sentiment Analysis — a concept popular amongst Machine Learning enthusiasts — is leveraged by companies to understand customer sentiment or emotion regarding the company’s products. Such an analysis crawls social media platforms to gather data on user sentiments on specific products by the company. It analyzes each user’s comment to classify it as positive, negative, or neutral, and to provide the overall result. The market has witnessed an exponential growth since 2016.

公司利用“ 情感分析” ( Sentiment Analysis)這種強大的功能來吸引客戶對公司產品的情感或情感，這種概念在機器學習愛好者中很流行。 此類分析會爬行社交媒體平臺，以收集有關公司針對特定產品的用戶情緒的數據。 它分析每個用戶的評論以將其分類為肯定，否定或中立 ，并提供總體結果。 自2016年以來，該市場呈指數增長。

傳統方法瓦解的地方！！ (Where the traditional approach falls apart!!)

I see Sentiment Analysis as a powerful technique that is yet to be fully tap-into. The current textual analysis has many shortcomings. “Yeah, no one does it better than you!”. The software will classify it as a positive sentiment. But what if it was Sarcasm? Here is another one. “…Amazon always does it”. What should I make of this comment? Let’s take a step back in the text and see a sentence before this. “Amazon is great in delivering products on time. Amazon always does it” — makes the latter sentence positive. However, “Amazon just delivered a damaged piece. Amazon always does it”- makes the text negative. Context is very crucial in understanding a sentiment. Text analysis has been missing the context of the conversation!

我認為情緒分析是一項功能強大的技術，目前尚未充分利用。 當前的文本分析有許多缺點。 “是的，沒有人比你做得更好！” 該軟件會將其歸類為積極情緒。 但是，如果是諷刺呢？ 這是另一個。 “……亞馬遜總是這么做”。 我該如何評價？ 讓我們退后一步，看看前面的句子。 “亞馬遜在按時交付產品方面很棒。 亞馬遜總是這樣做。 但是，“亞馬遜剛剛交付了一塊損壞的物品。 亞馬遜總是這么做”-使文字否定。 背景對于理解情緒至關重要。 文本分析一直缺少對話的內容！

Another problem is — we have been analyzing only textual data. There are more channels to explore. “iPhone 11 pro complete review” — a user searches on YouTube. From a teenager to an adult, everyone searches for a product review on the internet before buying it. “This is my review video on the new Bose’s Noise Cancellation headphones” — a passionate technology user on Twitter. Defeating word of mouth, word of such videos has become a direct influencer of people’s buying decisions. These videos are an excellent resource for businesses to seek user sentiment and feedback on their products.

另一個問題是-我們一直僅分析文本數據。 還有更多探索渠道。 “ iPhone 11專業版完整評論”-用戶在YouTube上搜索。 從青少年到成年人，每個人都在購買前在互聯網上搜索產品評論。 “這是我關于新型Bose降噪耳機的評論視頻” – Twitter上的一位熱情技術用戶。 這類影片的口碑不佳，已成為人們購買決定的直接影響者。 這些視頻是企業尋求用戶情緒和產品反饋的絕佳資源 。

歡迎來到語境情感分析的新時代-我的方法 (Welcome to the new age of context sentiment analysis — My approach)

In this article, I suggest a new approach towards Sentiment Analysis — Context-based hierarchical analysis of videos uploaded by product users. I decided to analyze not only text but also visuals and audio by extracting important attributes — facial expressions, speech tone, and voice intensity. To capture the context, we will analyze the smallest unit of speech separated by pauses — ‘’utterance’’. We want each utterance to seek information from the previous and the next utterance. Bi-directional Long short-term memory (LSTM) serves such requirements. A side benefit of LSTM– it resolved the vanishing/exploding gradient issue that I feared the network would face while learning long term dependencies. We want to analyze not only the video frames but also the changes in consecutive frames. 3D- Convolutional Neural Network was built just for the job!

在本文中，我建議一種新的情感分析方法-對產品用戶上傳的視頻進行基于上下文的層次分析。 我決定通過提取重要的屬性(面部表情，語音和語音強度)來分析文本，還分析視覺和音頻 。 為了捕獲上下文，我們將分析由停頓分隔的最小語音單位-“話語”。 我們希望每個話語都從上一個和下一個話語中尋找信息。 雙向長期短期記憶(LSTM)滿足了此類要求。 LSTM的附帶好處–解決了我擔心網絡在學習長期依賴關系時將面臨的消失/爆炸梯度問題。 我們不僅要分析視頻幀，還要分析連續幀的變化。 3D卷積神經網絡原為 專為工作而建！

Tackling one problem at a time, I developed the following algorithm:

一次解決一個問題，我開發了以下算法：

1. Extract Features for Text, Audio, and Video for each utterance

1.為每種話語提取文本，音頻和視頻的功能

• Text features from transcripts of spoken words using Convolutional Neural Network
  使用卷積神經網絡從口語筆錄中提取文字特征
• Audio features by using an open-source tool like OpenSMile
  通過使用諸如OpenSMile之類的開源工具的音頻功能
• Visual feature extraction using 3D-CNN
  使用3D-CNN進行視覺特征提取

2. For each channel (Text, Audio, and Video)

2.對于每個頻道(文本，音頻和視頻)

• Send the extracted features through a Bidirectional Long short-term memory (bi-LSTM) Neural Network to obtain context incorporating features

  通過雙向長短期記憶(bi-LSTM)神經網絡發送提取的特征，以獲得包含特征的上下文

3. Append context features of Text, Audio, and Video channels and feed to an LSTM network

3.附加文本，音頻和視頻通道的上下文功能，并提供給LSTM網絡

4. Send the output to a dense layer and then SoftMax layer for classification, using categorical cross-entropy on utterance’s SoftMax output for training

4.將輸出發送到一個密集層，然后發送到SoftMax層進行分類，使用話語的SoftMax輸出上的分類交叉熵進行訓練

5. After the training phase, pass the test set through the network to get context incorporating features

5.在訓練階段之后，使測試集通過網絡以獲取包含功能的上下文

6. Feed those features through SVM for classification

6.通過SVM提供這些功能以進行分類

The above approach incorporates the shortcomings of traditional text sentiment analysis and achieves a better accuracy of ~80% on MOSI data, which contains video reviews annotated by sentiment polarity.

上述方法結合了傳統文本情感分析的缺點，并在MOSI數據上獲得了約80％的更好準確性 ，該數據包含以情感極性注釋的視頻評論。

為企業創造價值 (Value creation for businesses)

A) To make a more informed decision regarding your brand and products

A) 對您的品牌和產品做出更明智的決定

Insightful sentiment analysis eliminates the guesswork involved in evaluating the performance of your products. Based on the insights, you can adjust to the current market needs, and increase your customer satisfaction. With such data in hand, precisely calculating customer-retention becomes easier. You can also use sentiment analysis to evaluate a new product concept before bringing it to life by putting the idea through concept testing and analyzing customer sentiment.

深入的情感分析消除了評估產品性能時的猜測。 根據這些見解，您可以適應當前的市場需求，并提高客戶滿意度。 有了這些數據，精確計算客戶保留率變得更加容易。 您還可以使用情感分析來評估新產品概念，然后通過概念測試和分析客戶情感將其付諸實踐。

B) To gain a competitive edge in the market

B) 獲得市場競爭優勢

Run the tool to get sentiments on your competitor’s products. Such knowledge will act as an incentive to keep up with the market and boost the performance of your products. It can also help you realize consumer trends early and leverage the same to get the edge in the market.

運行該工具，以獲取競爭對手產品的觀點。 這些知識將激勵您緊跟市場發展并提高產品性能。 它還可以幫助您及早實現消費者趨勢，并利用它們來獲得市場優勢。

C) To enhance customer experience

C) 增強客戶體驗

Many consumers share their experiences with the internet community through their online feedback. Their tone and temperament can be identified and labeled as a positive, negative, or neutral sentiment. So, you can know what is correctly implemented in your products, and what needs further improvement.

許多消費者通過在線反饋與互聯網社區分享他們的經驗。 他們的語氣和氣質可以被識別并標記為積極，消極或中性的情緒。 因此，您可以知道在您的產品中正確實現了哪些功能以及需要進一步改進的內容。