Multilingual Sentiment Analysis

Multilingual Sentiment Analysis

Emotional expression is subjective. It’s shaped by cultural background, emotional intelligence, individual personality traits and characteristics, and more. While we can communicate emotions clearly in verbal communication due to nuances of tone and inflection, it’s harder to determine how a person feels and what emotions they’re expressing through words alone.

Fortunately we can use sentiment analysis to determine the tone—and by extension, the emotion—that text conveys. In this demonstration, we’re creating an app that uses sentiment analysis to determine what emotions a person is experiencing based on the text input.

We’ll use the multilingual-22-12 model via the Cohere API—specifically the Embed endpoint—to generate embeddings for user-provided text in real time. Then, we’ll use these embeddings to train a classifier to predict the emotions the user expressed.

The multilingual-22-12 model generates embeddings for text data in over 100 languages. It transforms text data into text embeddings that capture the meaning and context of the text.

Text embeddings are numerical representations of words or phrases. They’re useful for sentiment analysis because they can capture the meaning and context of text more accurately than traditional methods like bag-of-words.

The steps for building the Sentiment Analysis application are:

Step 1: Gather Emotion Data
Step 2: Train the Emotion Classifier
Step 3: Get User Input
Step 4: Embed the Input
Step 5: Classify Sentiment
Step 6: Display Results
Step 7: Put It All together

Building a Multilingual Sentiment Analysis App

Step 1: Gather Emotion Data

We’ll use the XED dataset, consisting of 8 emotion categories, to train the emotion classifier. For this app, the embeddings for this dataset have already been created and stored as xed_with_embeddings.json, along with the labels for each data point.

Step 2: Train the Emotion Classifier

The bulk of the code that drives this application is stored in the file. Let’s open it and examine the train_and_save function line by line.

First, the function reads xed_with_embeddings.json into a data frame. In this data frame, column df.embeddings contains the embeddings for each sample and column df.labels_text contains the emotion label for each sample. The function transforms the embeddings for each sample into a list, forming data matrix X.

The function uses MultiLabelBinarizer from scikit-learn to one-hot encode df.labels_text and form y, which is a one-hot encoded label matrix. Next, the function executes the train-test split with the test set size configured at 1 percent of the total set. Afterwards, it instantiates a chain of classifiers with logistic regression as the base classifier.

A classifier chain is a technique for multi-label classification that involves training a chain of binary classifiers, one for each label. The output of each classifier serves as the input to the next classifier in the chain. Consequently, the final output is a vector of binary labels indicating the presence or absence of each emotion. This demo uses a chain of classifiers instantiated with logistic regression as the base classifier via ClassifierChain and LogisticRegression from scikit-learn. This allows us to predict the probability of each emotion class for each sample with chain_model.predict_proba in Step 5.

Subsequently, the train_and_save function fits the classifier on the train set and evaluates it with chain.score. This calculates the mean accuracy relative to the test set. In the last line, the function saves the trained classifier for determining emotion in a pickle file emotion_chain.pkl. A pickle file is a way of serializing and saving Python objects, such as trained machine learning models. By saving a trained model as a pickle file, we can reuse the model later without retraining it from scratch.

Then, we save the trained classifier for determining emotion into a pickle file named emotion_chain.pkl. To load the saved model later, we can simply use the pickle.load function in Python.

def train_and_save():  
  full_df = pd.read_json("./data/xed_with_embeddings.json", orient='index')  
  df = full_df  
  mlb = MultiLabelBinarizer()  
  X = np.array(df.embeddings.tolist())  
  y = mlb.fit_transform(df.labels_text)  
  classes = mlb.classes_  
  classes_mapping = {index: emotion for index, emotion in enumerate(mlb.classes_)}  
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.01, random_state=42)  
  base_lr = LogisticRegression(solver='lbfgs', random_state=0)  
  chain = ClassifierChain(base_lr, order='random', random_state=0), y_train)  
  print(chain.score(X_test, y_test))  
  pickle.dump(chain, open("./data/models/emotion_chain.pkl", 'wb'))

Step 3: Get User Input

Streamlit functions facilitate user input. Calling st.text_input creates a page object that asks the user “How are you feeling?” and captures the user’s text response. It then presents the user a slider via st.slider, which they use to select the number of top emotions they’d like to be presented (k).

feeling_text = st.text_input("How are you feeling?", "")  
top_k = st.slider("Top Emotions", min_value=1, max_value=len(classes_mapping), value=1, step=1)

Step 4: Embed the Input

The beginning of function score_sentence embeds the user input text into embeddings and converts it into a torch-based tensor.

embeddings = torch.as_tensor(get_embeddings(co=co, model_name=model_name, texts=[text]), dtype=torch.float32)

The function imports get_embeddings from the file. Then, the get_embeddings function calculates the embeddings by calling the multilingual-22-12 model.

def get_embeddings(co: cohere.Client,
                   texts: List[str],
                   model_name: str = 'multilingual-labse',
                   truncate: str = 'RIGHT',
                   batch_size: int = 2048) -> List[float]:

    @limiter.ratelimit("blobheart", delay=True)
    def get_embeddings_api(texts_batch: List[str]):

        for i in range(N_MAX_RETRIES):
                output = co.embed(model=model_name, texts=texts_batch, truncate=truncate)
            except Exception as e:
                if i == (N_MAX_RETRIES - 1):
                    print(f"Exceeded max retries with error {e}")
                    raise f"Error {e}"
        return output.embeddings
    st_pbar = tqdm(range(0, len(texts), batch_size))
    for index in st_pbar:
        texts_batch = texts[index:index + batch_size]
        embeddings_batch = get_embeddings_api(texts_batch)  #list(pool.imap(get_embeddings_api, [texts_batch]))
    return np.concatenate(embeddings, axis=0).tolist()

Step 5: Classify Sentiment

In the function setup within the tile, you can access the trained classifier model from emotion_chain.pkl where we saved it in Step 1.

def setup():  
 chain_model = pickle.load(open(model_path, 'rb'))

With the model stored as chain_model, we access it in score_sentence after acquiring the embeddings. The function then executes the model on the float tensor of embeddings to predict probabilities of emotion class(es), determined for each sample with chain_model.predict_proba. Then, torch.sort sorts the probability outputs for the emotion(s) associated with the user text input in ascending order.

In the last step, we convert the tensor of probabilities and tensor of associated indices from GPU-based to CPU-based, allowing for each tensor to be configured into a NumPy array. This then reverses both NumPy arrays such that the emotion with the highest probability is first. This sets up the loop we’ll run to display the images associated with the determined top k emotions experienced by the user.

outputs = torch.as_tensor(chain_model.predict_proba(embeddings), dtype=torch.float32) probas, indices = torch.sort(outputs)  
probas = probas.cpu().numpy()[0][::-1]  
indices = indices.cpu().numpy()[0][::-1]

Step 6: Display Results

Let’s first head back to the setup function we discussed in step 5. The first part of this function initiates emotions2image_mapping as a dictionary and each of the eight emotion labels are mapped with file paths corresponding to respective emotion GIFs. Then, emotions2image_mapping iteratively filled with the emotion gifs themselves utilizing the file paths.

emotions2image_mapping = {  
 'Anger': './data/emotions/anger.gif',  
 'Anticipation': './data/emotions/anticipation.gif',  
 'Disgust': './data/emotions/disgust.gif',  
 'Fear': './data/emotions/fear.gif',  
 'Joy': './data/emotions/joy.gif',  
 'Sadness': './data/emotions/sadness.gif',  
 'Surprise': './data/emotions/surprise.gif',  
 'Trust': './data/emotions/trust.gif',  

for key, value in emotions2image_mapping.items():  
  with open(value, "rb") as f:  
  emotions2image_mapping[key] =

Now circling back to the score_sentence function, the call returns emotions2image_mapping to setup and is afterwards accessed in the loop inside score_sentence to display the top k emotions to the user as GIFs.

The loop iterates through each of the top k emotions, with the index and probability associated with each emotion is extracted as tuple (index, p) at each iteration of the loop. Before the start of the loop, we use Streamlit to create k columns that will display the top k emotion(s) to the user.

In the first iteration, a row of three columns is created via st.columns. Once three columns are filled with GIFs corresponding to ranked emotions, a new row of three columns is created in the following iteration. Structuring the GIFs to be displayed in rows of up to three emotions facilitates user readability.

cols = st.columns(top_k, gap="large")
for i, (index, p) in enumerate(zip(indices[:top_k], probas[:top_k])):
  if i % 3 == 0:
    cols = st.columns(3, gap="large")

    emotion = classes_mapping[index]

    i = i % 3
    image_file = emotions2image_mapping.get(emotion, None)
    if image_file:
      image_gif = base64.b64encode(image_file).decode("utf-8")
        f'<img src="data:image/gif;base64,{image_gif}" style="width:250px;height:250px;border-radius: 25%;">',
      cols[i].markdown(f"**{emotion}**: {p * 100:.2f}%")

      print(f"Predicted emotion: {emotion}, with probability: {p}")


In this tutorial, we learned how to use embeddings to build a text classifier that can determine emotions. It is important to note the layers of this process. We first generated the embeddings by transforming the text via the multilingual-22-12 embedding model, and then we built the multi-label classifier. When we later predicted and displayed emotions to the user, we followed the same layered process of calculating embeddings and then applied the classifier to produce probabilities of detected emotions.